{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating the similarity matrix and using it to fit an SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import utils\n",
    "import turicreate as tc\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">x1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">x2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[7 rows x 3 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tx1\tint\n",
       "\tx2\tint\n",
       "\ty\tint\n",
       "\n",
       "Rows: 7\n",
       "\n",
       "Data:\n",
       "+----+----+---+\n",
       "| x1 | x2 | y |\n",
       "+----+----+---+\n",
       "| 0  | 0  | 0 |\n",
       "| -1 | 0  | 0 |\n",
       "| 0  | -1 | 0 |\n",
       "| 0  | 1  | 1 |\n",
       "| 1  | 0  | 1 |\n",
       "| -1 | 1  | 1 |\n",
       "| 1  | -1 | 1 |\n",
       "+----+----+---+\n",
       "[7 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = tc.SFrame({\n",
    "    'x1': [0,-1,0,0,1,-1,1],\n",
    "    'x2': [0,0,-1,1,0,1,-1],\n",
    "    'y': [0,0,0,1,1,1,1]\n",
    "})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pZn3Mnr/vSnol8n2jcdF5mvMcepekMx0cXz85JmlP8Wmdv5L00cwj15ZFxMBukr6lW/9afiLpA0nHi/NflPTSrHbfkPQ/ulWtPtbtcXdhnu7RrU/nvFO8rizOVyQ9Xez/jaS3JP26eN3b7XF3cH7mrQ9Jj0vaVezfJenfJE1I+m9Jm7o95h6dp+9LOl2soV9K2tLtMXdpnp6VVJP0f0U+7ZW0X9L+4rp16xNP7xbvtUpZffOrFQAgCR7pAEASBD4AJEHgA0ASBD4AJEHgA0ASBD4AJEHgA0AS/w9TFb+rl7G0IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.plot_points(pd.DataFrame(data['x1','x2']), pd.DataFrame(data['y']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating the similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(x, y):\n",
    "    return np.exp(-(x[0]-y[0])**2-(x[1]-y[1])**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">x2</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">y</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim0</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim1</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim2</th>\n",
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       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "    </tr>\n",
       "</table>\n",
       "<table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim4</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim5</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">Sim6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.01831563888873418</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.1353352832366127</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.006737946999085467</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.00033546262790251185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.36787944117144233</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.00033546262790251185</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.0</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[7 rows x 10 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tx1\tint\n",
       "\tx2\tint\n",
       "\ty\tint\n",
       "\tSim0\tfloat\n",
       "\tSim1\tfloat\n",
       "\tSim2\tfloat\n",
       "\tSim3\tfloat\n",
       "\tSim4\tfloat\n",
       "\tSim5\tfloat\n",
       "\tSim6\tfloat\n",
       "\n",
       "Rows: 7\n",
       "\n",
       "Data:\n",
       "+----+----+---+---------------------+----------------------+\n",
       "| x1 | x2 | y |         Sim0        |         Sim1         |\n",
       "+----+----+---+---------------------+----------------------+\n",
       "| 0  | 0  | 0 |         1.0         | 0.36787944117144233  |\n",
       "| -1 | 0  | 0 | 0.36787944117144233 |         1.0          |\n",
       "| 0  | -1 | 0 | 0.36787944117144233 |  0.1353352832366127  |\n",
       "| 0  | 1  | 1 | 0.36787944117144233 |  0.1353352832366127  |\n",
       "| 1  | 0  | 1 | 0.36787944117144233 | 0.01831563888873418  |\n",
       "| -1 | 1  | 1 |  0.1353352832366127 | 0.36787944117144233  |\n",
       "| 1  | -1 | 1 |  0.1353352832366127 | 0.006737946999085467 |\n",
       "+----+----+---+---------------------+----------------------+\n",
       "+----------------------+----------------------+----------------------+\n",
       "|         Sim2         |         Sim3         |         Sim4         |\n",
       "+----------------------+----------------------+----------------------+\n",
       "| 0.36787944117144233  | 0.36787944117144233  | 0.36787944117144233  |\n",
       "|  0.1353352832366127  |  0.1353352832366127  | 0.01831563888873418  |\n",
       "|         1.0          | 0.01831563888873418  |  0.1353352832366127  |\n",
       "| 0.01831563888873418  |         1.0          |  0.1353352832366127  |\n",
       "|  0.1353352832366127  |  0.1353352832366127  |         1.0          |\n",
       "| 0.006737946999085467 | 0.36787944117144233  | 0.006737946999085467 |\n",
       "| 0.36787944117144233  | 0.006737946999085467 | 0.36787944117144233  |\n",
       "+----------------------+----------------------+----------------------+\n",
       "+------------------------+------------------------+\n",
       "|          Sim5          |          Sim6          |\n",
       "+------------------------+------------------------+\n",
       "|   0.1353352832366127   |   0.1353352832366127   |\n",
       "|  0.36787944117144233   |  0.006737946999085467  |\n",
       "|  0.006737946999085467  |  0.36787944117144233   |\n",
       "|  0.36787944117144233   |  0.006737946999085467  |\n",
       "|  0.006737946999085467  |  0.36787944117144233   |\n",
       "|          1.0           | 0.00033546262790251185 |\n",
       "| 0.00033546262790251185 |          1.0           |\n",
       "+------------------------+------------------------+\n",
       "[7 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_with_similarities = tc.SFrame(data)\n",
    "\n",
    "for i in range(len(data)):\n",
    "    column = []\n",
    "    for j in range(len(data)):\n",
    "        first = [data[i]['x1'], data[i]['x2']]\n",
    "        second = [data[j]['x1'], data[j]['x2']]\n",
    "        column.append(similarity(first, second))\n",
    "    data_with_similarities = data_with_similarities.add_column(column, 'Sim'+str(i))\n",
    "data_with_similarities"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting a (linear) SVM to the similarity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre>SVM:</pre>"
      ],
      "text/plain": [
       "SVM:"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of examples          : 7</pre>"
      ],
      "text/plain": [
       "Number of examples          : 7"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of classes           : 2</pre>"
      ],
      "text/plain": [
       "Number of classes           : 2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of feature columns   : 9</pre>"
      ],
      "text/plain": [
       "Number of feature columns   : 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of unpacked features : 9</pre>"
      ],
      "text/plain": [
       "Number of unpacked features : 9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Number of coefficients    : 10</pre>"
      ],
      "text/plain": [
       "Number of coefficients    : 10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>Starting L-BFGS</pre>"
      ],
      "text/plain": [
       "Starting L-BFGS"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>--------------------------------------------------------</pre>"
      ],
      "text/plain": [
       "--------------------------------------------------------"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy |</pre>"
      ],
      "text/plain": [
       "| Iteration | Passes   | Step size | Elapsed Time | Training Accuracy |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 0         | 3        | 5.000000  | 1.009461     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 0         | 3        | 5.000000  | 1.009461     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 1         | 7        | 1.250000  | 1.012217     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 1         | 7        | 1.250000  | 1.012217     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 2         | 9        | 1.036763  | 1.015046     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 2         | 9        | 1.036763  | 1.015046     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 3         | 10       | 1.036763  | 1.022584     | 0.285714          |</pre>"
      ],
      "text/plain": [
       "| 3         | 10       | 1.036763  | 1.022584     | 0.285714          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 4         | 13       | 0.090141  | 1.037391     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 4         | 13       | 0.090141  | 1.037391     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>| 9         | 22       | 0.690665  | 1.060519     | 1.000000          |</pre>"
      ],
      "text/plain": [
       "| 9         | 22       | 0.690665  | 1.060519     | 1.000000          |"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre>+-----------+----------+-----------+--------------+-------------------+</pre>"
      ],
      "text/plain": [
       "+-----------+----------+-----------+--------------+-------------------+"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svm = tc.svm_classifier.create(data_with_similarities, target='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\"><table frame=\"box\" rules=\"cols\">\n",
       "    <tr>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">name</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">index</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">class</th>\n",
       "        <th style=\"padding-left: 1em; padding-right: 1em; text-align: center\">value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">(intercept)</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.35621896975099737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">x1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.41253063760515934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">x2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.41253063760515957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim0</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-1.3632790186264576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.8491440337159405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim2</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">-0.8491440337159405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim3</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.4748876391056869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim4</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">0.47488763910568627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim5</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.022208179982977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">Sim6</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">None</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1</td>\n",
       "        <td style=\"padding-left: 1em; padding-right: 1em; text-align: center; vertical-align: top\">1.022208179982976</td>\n",
       "    </tr>\n",
       "</table>\n",
       "[10 rows x 4 columns]<br/>\n",
       "</div>"
      ],
      "text/plain": [
       "Columns:\n",
       "\tname\tstr\n",
       "\tindex\tstr\n",
       "\tclass\tint\n",
       "\tvalue\tfloat\n",
       "\n",
       "Rows: 10\n",
       "\n",
       "Data:\n",
       "+-------------+-------+-------+---------------------+\n",
       "|     name    | index | class |        value        |\n",
       "+-------------+-------+-------+---------------------+\n",
       "| (intercept) |  None |   1   | 0.35621896975099737 |\n",
       "|      x1     |  None |   1   | 0.41253063760515934 |\n",
       "|      x2     |  None |   1   | 0.41253063760515957 |\n",
       "|     Sim0    |  None |   1   | -1.3632790186264576 |\n",
       "|     Sim1    |  None |   1   | -0.8491440337159405 |\n",
       "|     Sim2    |  None |   1   | -0.8491440337159405 |\n",
       "|     Sim3    |  None |   1   |  0.4748876391056869 |\n",
       "|     Sim4    |  None |   1   | 0.47488763910568627 |\n",
       "|     Sim5    |  None |   1   |  1.022208179982977  |\n",
       "|     Sim6    |  None |   1   |  1.022208179982976  |\n",
       "+-------------+-------+-------+---------------------+\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm.coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype: float\n",
       "Rows: 10\n",
       "[0.35621896975099737, 0.41253063760515934, 0.41253063760515957, -1.3632790186264576, -0.8491440337159405, -0.8491440337159405, 0.4748876391056869, 0.47488763910568627, 1.022208179982977, 1.022208179982976]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coefs = svm.coefficients['value']\n",
    "coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def svm_rbf(p):\n",
    "    similarities = [similarity(p, [row['x1'], row['x2']]) for row in data]\n",
    "    return np.dot(similarities, [-1,-1,-1,1,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = np.array(pd.DataFrame(data['x1','x2']))\n",
    "labels = np.array(data['y'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model(X, y, model):\n",
    "    X = np.array(X)\n",
    "    y = np.array(y)\n",
    "    plot_step = 0.1\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),\n",
    "                         np.arange(y_min, y_max, plot_step))\n",
    "    Z = np.array([model(i) for i in np.c_[xx.ravel(), yy.ravel()]])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.contour(xx, yy, Z,colors = 'k',linewidths = 3)\n",
    "    plt.contourf(xx, yy, Z, colors=['red', 'blue'], alpha=0.2, levels=[-20,0,20])\n",
    "    utils.plot_points(X, y)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ou2XmNdzcrkvHdeu2yBdK89atc5KyNzQ0ZuHCfflCbpm8QePG7dm79yHNmztLddevn2LQoIa8e/dGg5J9PoIg0K3bSFxc7lC+fDWp/vr1U/ToUZWrV0+mc7VMWhRIhe/u/lHhV61aX4OSZJ7du1dJx+3a9f/iFsmWyT7GxqbMm7ebH3/8Rap79syd/v3rqCzfmd+wt3dky5Z/6dVrvFT3/r0/Y8e2ZfHi0Sqjf5n0KXAKXxRF3NyuSeVq1fK+wvf2fs7Vq8elcteuIzUojUx+RktLixEj5jJjxia0tZUT/YGBvgwe3JiLF49oWLrPR09Pn3HjlvL772ewtPyYGHDPntX06ePEkyf3NChd/qHAKXxf31dSHhIjo8LY2TlqWKKM2bt3jTQRVb9+6wKzGIaM5ujQ4UfWrPlbSrgWHR3Jzz9/z65dKzUsWfaoW7cFu3e706hRe6nOy+sh/frVZuPG3/LtRHVuUeAU/qNHt6XjypXr5Avf9X/++ehG5+w8XIOSyBQknJyasnnzDYoXtweUb79Ll47l2LEtmhUsm5ibW7Ns2RGVCN34eAXr1k1nwIB6eHk90rCEeZcCp/CT5xXPL3bw5DLb2VXUoCQyBY2kgKbkc1m//TaYmzfPalCq7JMUobtz532qVKkr1T965Erv3l+zbdtilQXVZZQUOIVfuPDHQJTQ0DyZzy0VxYrZScdv377UnCAyBRJzc2tWrTpFuXJVAeVoeOLEzjx6pP71onOb0qXLs2HDFUaOXICurh6gzMezatVEBg9uxKtXHhqWMG9R4BR+8sjDDx/yh8IvUeIr6djHx0uDksgUVIyNC7NixQkpF1NkZDijR7fm5cunGpYs++jo6NC//yRcXO6orGDn7n6Dnj2rs2vXylQrin2pFDiFn3yEn5RaIa+TZGMFpceOjExOYGtbgtWrz0iDopCQQEaObPHJhdXzG2XLVmbLlpsMHfqr5KEUExPF0qVjGTLkmwLxcMsuBU7hJx/h5xeTTvIR/qtX8pdSJuewt3dkxYoT0poQ7969ZtSoVkRHR2pYMvWgo6PL4MEz2Lr1lspCKvfvX6Vnz2ps3PgbcXGxGpRQsxQ4hW9kVFiauY+KisDDw03DEmVM8onaS5eOcPXqCQ1KI1PQqVKlLosWHZBGwV5eD1m9erKGpVIvFSrUwMXFlYEDp0l/Z2xsDOvWTadnz+rcu3dFwxJqhgKn8AVBoHHjDlL5wIF1GpQmc1SsWJNatZoBSte5qVN7ypNNMjlK/fqtmDx5rVTes2c1t2+f16BE6kdXV4+ffpqDi4srjo4fU8u8ePGYwYMbM2fOoHwzz6cuCpzCB1Vf9lOnthMeHqpBaTJGEATmzdtNkSKlAIiICGXChE55Xm6Z/E2nToNo2LCtVP711x8L5HeufPlqbN58kwkTVmJoaCzVHznyF126VODUqR1fTAbOAqnwa9RohL29chXFyMhwTp/eqWGJMsbc3JolSw5JCdNevHjMrFn9ZO8CmRxDEASmTdsgzXu9e/eaZcvGaViqnEG5eMxo9u17TNOm30v1wcEBTJ/emxEjWvD69TMNSpg7FEiFLwgCnTsPlcoHD67PF0/wChW+ZurUDVL54sXDbNgwO1/ILpM/sbIqyqRJH007R49u4sqV4+lckb+xtS3B4sUHWbLksMpyobdu/UO3bpVZv34W0dFRGpQwZymQCh+gTZve0mjZw+N+vhjlg1Lu5Mu8bdjwK2PGtMXbW/bPl8kZWrToxnffdZXKCxYMR6GI06BEOU+TJh3Zu/cRPXuOQ0tLqQbj4mLZsOFXunZ15OLFwwVyoFVgFX7hwua0bt1bKs+ZM5D//vtXgxJlnjFjFkuTuKDM/92tWyU2b54vr+0pkyNMmrQWMzPlMtN+fm+4dOmohiXKeYyMTBg/fhnbtrlSqVJtqf7t25dMmPA9o0a1KnC++wVW4YNScZYpUwFQumT9738d88ViEDo6OixdegRn5xEkLgVMTEw0a9b8wvffl+Pw4Y2fNQILDPSlY5cKBAa+U7fIXzQF4XM1M7Pkhx+GSeV9+9ZoUJrcpUKFGmzadJ1fflmPqamlVH/z5t90716FlSsnEhERpkEJ1Ye61rTdJAiCvyAIaa4yLChZJQiCpyAI7oIgfJ1WO3VjYmLG8uXHpEmpoCA/xo/vQFRURG7cPlsYGhozadLvbNnyLw4ONaR6P783/PbbYJydHTl1akeWEkRtdFnE2w/+bHRZlBMif7EUlM+1c+ehUnZZV9cLPHvmrmGJcg9tbW06dx7CwYMeODsPl8w8CkUcLi6L6dy5fIHw5lHXCH8L0Cqd862BconbEOAPNd03Q0qWLMvChR+DTDw87jNjRp984/1SqVIttm69xYQJqzA3t5bq37zxZPr03vToUY3z5w9m+EUMDPTl2ImtiOfOcezE1nw9Gs1LFKTP1da2BN9800kq//nnLM0JoyFMTS2YNGkNLi53qF69oVQfFPSO6dN7M3RoEzw9H2hQwuyhFoUviuJlIL0Iho7ANlHJTcBMEISi6rh3ZnByasKUKR+fMRcuHGLhwhH5xh6uo6ND9+6jOHLEixEj5kmLWoAySnLixB/o1q0yGzfOwdPzQZrKf6PLIhL69oMaNUjo2zffj0bzCgXtcx04cJp0fOHCIZ48uatBaTSHg0N1Nmy4zJw527Gy+qiq7t69TK9eNViyZAyhocEalPDzENT1iiIIQhnguCiKldM4dxxYIIri1cTyOWCSKIrp5md1dHQSXVzUl8J12bLx7Ny5XCqXL1+duXN35rsc9GFhIezYsYydO5cTGRme6ryVVVHq1PmOunVbULt2c0QxgY7dKxHz6CEULQq+vug7VubI7odYWRVJ4w4ymSEw0LdAfq6TJjlz7tx+APr1m8SoUQs0LJFmiYgIY+PG2ezcuYL4+I8rapmZWTF8+Fw6dhyYpxZacnIS7oii6JTWudyatBXSqEvzSSMIwhBBEFwFQXANDg5QqxBjxiymWbMfpLKHx3169/6aXbtW5qvFEkxMzBg2bDZHjnjRp88EKXdQEoGBvpw4sY3p03vTsmURuveoRtxX9vDoEURHQ9GiBWI0qmmk0X3RxBFgAflcGzVqJx2/e/dag5LkDYyMTBgzZjG7drmpeM+FhAQyb95Q+vatxf3719LpIe+QWwrfGyiZrFwCeJtWQ1EU/xRF0UkURafkNmt1oK2tzYIFe5kwYSV6evqA0vtl6dKxDBzYAE/PNOec8yzm5taMGbOYs2f9mTdvN82bO1O4sHmqdiHBASTcuQPNm4O5OTRqRFxMFIcP/cl//93O9xNRmiDJdh83eaJKfdzkifnelm9tXVw6Dgjw0aAkeQt7e0fWrv2HhQv3S2lQAJ4+vcegQQ2ZNq0XgYG+GpQwY3LLpNMWGAm0AeoAq0RRrJ2yXUrUbdJJjqfnA6ZN66UyAaOjo0v//pMZMGCq9EDIb8THx/P48R1u3vybf//9m/v3ryGK6U9Qm5tbU6lSbSpXrkOlSrWpWLGm5JMtkzYLlo/jiCXErVye6pzumHF0DBKYPG6ZBiTLPi9ePMbZ2RFQpu4+fNhTwxLlPaKjI9m2bTFbty4gJiZaqjcyKsxPP/2Gs/NwjZl50jPpqEXhC4KwC2gCWAF+wExAF0AUxXWC0pn8d5SePJHAjxnZ7yFnFT4oI+u2bFnAX3/9puLXXqZMBSZP/oOaNb+R/ODzI4GBvnTo5kjsiuVw5w78/Td4ZC4Lp4WFDfb2lRI3R0qU+IqiRctQpEgpKYL5SyWV7T4l+dyWHx4eSpMmyvWg9fUNuHo1Ml//DnISX99XrFz5M//8s0+l3sGhBlOmrKNy5QzHtWonxxV+TpHTCj8JL69H/PbbYNzdr6e4fy26dx9N8+bO+XLEn+Yo1NcXbt36uF2+DLFZWxDC1NSCYsXssLIqmmwrplK2tLRFR0dXzX9R3iC90X0S+XmUHx0dRZMmptIg6J9/AjEzs8zgqi+bW7fOsXDhCJUFjARB4IcfhqXyrMtpZIWfCRISEti//w9+/31yKs8Xc3NrWrbsQZs2fahYsWa+Ge207mRPgPeLDNuZ2xZnzE/zePjwFg8f3uL58/+Iicl+Aikjo8KYmVlhamqJmZll4l5ZNjW1xMTEHGNj08StsHRsaGgiBb7kRTL7uVqXsOPU4fyVA0kURaZN68WZM7sAZU75s2cDMDYurGHJ8j6xsTG4uCxh06bfVMw8FhY2jB27lNate+WK7pAVfhZ49+4N69fP5MyZnWn66dvZVaRNmz60bt2bIkVKptFD/ic+Ph5f35d4eT3Cy+shL18+4e3bl/j6vsTP7w1aWto5mlxLEAQMDU0Slb8xhQoZJ+6Nkh0r9wYGhujrF0pzb2BQCH195aanZ4C+voG019bWyTcP7pwmPDwUV9fz3Lz5NzdunMHH5+NDasKEVXTvPkqD0uU/vL29WLx4FNeunVSpd3JqyuTJf1CmjEOO3l9W+J9BcHAAhw5t4MCBP9Jc5FkQBGrWbELbtn1p1uwHjIxMNCBl7qNQxBEY6EtAwFsCAt4SGOibagsK8iU4OCBPRzNraWmpPAT09PTR1dVHT08/VTnlPu1zep9sp6url27/enr6ufoAio+P58mTu9y4cYZ///0bd/cbKv7lSXTuPJQpU/6QH4yfgSiKXLhwiKVLx6joDz09fYYM+ZXevf+Hjo5OjtxbVvjZID4+HlfXC5w86cL58wfSzMOjr1+I+vVb0ahRexo2bIuFhY0GJM1bJCQkEBYWwocPQYSEBKrsk47Dwz+kueWHXEfqRhCEVA+SlA8KXV09dHT00NHRRVtbBx0dHelYWdZFS0ub+HgFcXGxKBSxKBRxxMXFSptCEcurVx58+BD0SVkKFTKiffsfGT9+WYGdh8ktIiLC+PPPWezerRrrU7FiTWbO3Kyy0Lq6kBW+moiKiuDChUOcOLGN27fPpTmCFQSBSpVqU6NGI6pWrU+VKvXypaeGJlEoFEREhBIREUpUVARRUeFERoarHCvL4URHRxITE5XmXrlFERsbTWxsNDExSfuoPP32oQkcHGpQr15L6tZtSbVq9dHV1dO0SAUKDw835swZyOPHd6Q6HR1dBg6cxo8/TlHrg1VW+DmAv78Pp0/v5MSJbTx/nn7AVvHi9lSpUpfy5atTvnw1ypevLr8FaBiFQqHyIIiLiyE2NmmLJjY2RqpLfi5lXdrnYjPZ7uP9cvsBZGlpS506LahXryV16nwnfx9zAYVCwfbtS/nzz5kq84OOjk7Mnu0ipXLPLrLCz0FEUeTVq6dcvnyMK1eO4eZ2LVM/XkvLIjg4VKdcuWqUK1cNB4fqlCxZVn6F/kJRKBTExSkfFrGxMSgUsdIDQvU4DoUijvh4xSf3Ojq66OjoJZqAdBPnF/Qkk5CpqQVlylSQbfMa4uXLJ8yePQB39xtSnb6+ASNGzKd799HZ9lCTFX4uEhIShJvbNdzdr+Pufp1Hj26ruGilh7a2NsWK2VGyZDlKlSqnsi9atHSeStAkIyPz+cTHx7N9+1LWrZtOXNzHOJiaNZvw669bVVI3ZBVZ4WuQuLhYnj69x5Mnd/HwcMPD4z6eng+Ijo7MUj86OroUK2ZHkSKlsLUtSZEiJbG1Vd2+FE8hGZmCgqfnA2bM6IuHx32prnBhc2bM2ESTJp3SufLTyAo/jxEfH8+bN548e6Z8AHh4uOHp6Z6m+2dWMDY2xdq6GBYWtlhY2GJlVUQ6trS0xdzcBlNTSwoXVgY85eXgJhmZLwXl4umz2bJlvoo5uGvXkYwZszjLqUxkhZ9PiIqKwNv7Oa9fP+PNm2cq+6Ag9WZfFAQBI6PCicrfjMKFzTExMZOiX42MCmNkZIKRUWEMDZV7Y+OPx0ZGJhQqZIyenr5sC5aRUQNubteZOrWHSkrqihVrsnjxwSyZeGSFXwCIiAjj7dsX+Pm94d27N/j5qW7+/t4aWcFLW1s7VfRroUJJUbFGnzxWRskq9ymvNTQ0RldXT36QyHxxhIYGM2fOQC5cOCTVmZtbM3/+XpycmmSqD1nhfwGIokhwcABBQe8ICvLj/XvllnQcFPSOkJBAQkICCQsLISIiVNMip4u2tg6FChlJbxNJbxbKlAvKfVL6haS3k9j6OyYAACAASURBVLTeVHIqmlFGJqcQRZG9e9ewbNk4KQJaW1ubceOW0a3bqAwHQrLCl0lFUnBTWFgwYWEhhIYq92FhwYSHfyAyMozw8FAiI8OkIKiIiLBUAVF5fV1gQ0NjzMysMDOzxtxcuaUsW1jYYmNTHEvLIrInlEye4f79q0ya1IWgID+prm3bvkyZsg4Dg0KfvE5W+DI5hkIRR1RUhBT5mhQFGxkZlvhQiCA6OkJqk/xYNYpWdZ+Tydk+hZaWFlZWRbG2Lo6NTQlsbD7ule6yZTE3t5ZNTdkgISGB+/evcvr0Tu7du4yWlnYqk1+Sua9QISMsLGypVKkWDg418mWK8uzi5+fNxIk/8PDhLamuYsWaLF16BBub4mleIyt8mXxHXFys9OBI/nahLCd/6wiV3k7Cw5VvKh/3H9S+fKOhoTElSpSlZEnlVqJEWUqU+IpSpcphbV1Mfhh8goSEBLZtW8y+fb9/ljearq4e5ctXx9HRia++qkLZslUoW7YyxsamOSBt3iImJpqFC0dw9Ogmqc7GpjjLlx/HwaF6qvaywpf5IklISCA8/IM0dxEcHEBwcAAhIQEqx4GB7wgI8CE4OCBb9zM1taBs2aqUL6+Mni5fvhp2do5f/AphAJs2zWPt2qlq77dIkVJUq9aA+vVbU79+K9S9DnZeQRRF9u//gyVLRktJ2IyMCrNy5UmqV2+g0lZW+DIymSA2NoaAgLf4+3vj7+8j7f383uDj44W3tycREWFZ6lNbW5vSpR0oV64ajo61qFWrGWXLVvmiYiC8vB7Rq1cNKaLUzMyK5s2dad68K6amFirmvSQzYJJp780bT/777yZv3mS8rq4gCFSs6ESDBm1o0KANjo5OBe5z/vfff5g0qQvh4R8AMDAwZMmSw9St+53URlb4MjJqQBRFQkICefPGkzdvPPH2fo63t/L4xYvHmfZ8MjW1pGbNJtSq1YxatZpRurRDgTUFxcfHM2hQQx48uAkolw3966+rWc7GGRISyMOHt3n2zA1Pzwd4ej7g5csn6c71mJlZUa9eKxo0aEPjxu0xNDTO1t+SV3j2zJ0RI77j/Xt/QGnumj9/L02adARyZxHzVsBKQBvYKIrighTn+wOLAZ/Eqt9FUdyYUb+ywpfJL4iiiK/vKzw83Hj2zI1nz9x59swtUyNTK6ui1KrVDCenZjRs2BZLS9tckDh32LFjOcuXjweU6UG2b79L2bKV1dJ3XFwsz5//x/Xrp7l+/RTu7tc/mbjQ0NCYFi2607HjQCpXrpPvH7CvXnkwfHhz/PzeAMo3yVmzttK6da+cVfiCIGgDHsB3gDdwG+ghiuKjZG36A06iKI7MSt+lSzuIkyevRRTFxMk3UTpOXpeQkJBmG+U/P/U1opiQTh8Jqa5PeS6t8qfaZfZ6VZlU26X3N6aUOYlPHaf430lffNW9gJaWFlpaWgiC6j7pOGkBjqTFN5IvwpF0LqOVogwMDClUyEhlX9BewSMjw/H0fMDTp/e4e/cyrq7n050r0NLSombNJnz3XTeaNeuMmZlVLkqrXry9n9OtWxVpfeShQ39l8OAZOXa/0NBg/v33LNeuneTGjdMq7ozJsbd3pEOHAbRp0ydfp4X29X3F8OHNpUGFIAhMnvwH8+cPy1GFXw+YJYpiy8TyFABRFOcna9Ofz1D4giDkXXuTTI6gr2+AgYFS+RsaGidb5NwMY2PTxAArU+nY1NQSCwsbzM1tMDe3zvOue6Io8vz5Q1xdz3P79nnu3Lko2WNToq2tQ8OGbenYcSD167fOd0FkI0e25ObNvwEoV64q27bdzrWFVRISEnj69B7Xrp3k1KkdvHr1NFUbbW0dGjRoQ9u2fWnUqF2e/+6kRWCgLyNGtEi5JkeOKvwuQCtRFAcllvsAdZIr90SFPx8IQPk2ME4UxTef6G8IMCSxWDNbwsl8cRgbm0oPAGXiOBtsbUtStGhpihQpRdGipbGyKpZnlGd8fDxPn97D1fUCV64c4/79q2m+kVlaFqFdu3507DiQUqXKaUDSrBEeHkqTJkqXSS0tLbZuvUXFipr5OYuiiJvbdY4e3cTZs3vSXELTwsKG3r0n0KXLT/nO1h8SEsTo0a159Oh2UlWOKnxnoGUKhV9bFMVRydpYAuGiKMYIgjAM6CqKYrOM+jYxMhJrOTpKpgeBj2aIpE0reTnZeS0trYzbJ16jNFOonkt+vZaWlnQuZdukdmnWp+wznes/dZ/kfaf1GaT6e5LZJpNbKVPaLEVRREx2LNUlbShHSQkJCSQkmpeS9vEJCcTHx6NI3OIUCulYkXgcGxdHrEJBdEwMMXFxxMTGftwnHkfFxBARFUVEVBSR0dFERmdu3YDsoq2tjY1NCYoUKUWRIqUpUcIeOztH7O0dKVWqvEZHev7+Ppw7t5+zZ/eoLJCRhCAItGrVi6FDZ1GixFcakDBzvHv3mnbtSgNga1uCEyfSHN/lOhERYZw9u5ejR/9K8/M1NbWkd+//4ew8AmPjwhqQ8PMIDw9l9OjWuLtfhxxW+BmadFK01wbei6KYYcSEk6Oj6Oriki35ZPIPCQkJRMfGSg+B8MhIPoSH8yE8nJCkfVjYx7qwMAJDQvAPDsb//XsCQkJUFor+HLS1tSlRoiz29pWwt3eU9nZ2FXN9NbKXL59y9OgmTpzYmsoera2tQ6dOgxg0aDrW1sVyVa7M4On5H927Kxfotrd3ZO/ehxqWKDWvXz/j1KntHD26WZr8TKJwYXN69hxP9+6j8k1wV2hoMBcvHmb27AE5qvB1UJppvkXphXMb6CmK4sNkbYqKouibePw9MEkUxboZ9S0rfJmskJCQQHBoqPQA8Hv/nndBQbx+945Xvr7K/bt3+AUFZblvff1CVKpUi6pV6ydu9XJtQlWhiOPatVMcOPAH16+fTiVXt26j6Nt3ImZmlrkiT2Zwc7vOwIHKgKAqVeqyeXPq0XReIS4ulhMntrFp01zevn2pcs7Y2JQePcbSo8cYChc214yAWSQ33DLbACtQumVuEkVxriAIswFXURSPCoIwH+gAKID3wE+iKD7JUHBZ4cvkANExMbx+9056EHi8fs2jFy946OXFCx+fjDtIpFSp8lSrVp9q1RpQs2YTSpYsm4NSK7l//xpr1/7C3buXVeqNjArTp8/P9O79v3QTa+UW16+fZvTo1gDUrduC338/o2GJMkahiOPkye1s2jQXb+/nKueMjArTv/9k+vSZkOfXnc63gVeywpfJbSKionjy8iUPnz+XHgLuz57x+l3GC9DY2VWkadPONGvWGQeHGjnm6y2KIjdv/s2aNb/w5MldlXOlSpVnxoxNqcLtc5uzZ/cyZUo3AJo1+4FFi/ZrVJ6soFAoOHNmF3/99RuvX3uonCtXriozZmzS2AR0ZpAVvoxMNnkbEMANd3duPHjAdXd37jx+TGzcp6M8ixYtLSn/KlXq5UjaZVEUOXfuAOvWTefly48vzMrc6cvp1m2kxgKMzpzZzdSpPQBo3tyZBQv2akSO7BAfH8/Zs3vYuHFOqs+3T5+fGTx4Zp7MkyQrfJlMI4oi0TExktdMZHQ0RaysMDVWdVU7cvEi70NDU3nnJG0JCQloaWmhraWFc/Pm2BVXTeW65dgxEhISKKSvj6GBAYUMDJT7pLK+PkaFCmFuYpJnXCiTEx0Tw90nT7jm5sbFO3e44OpKVEzaawNYWtryzTedaNGiOzVrfqN2JaxQKDh8eCOrV09SSe/Qvn1/Jk/+QyNK6fLlY4wf3wGAhg3bsmLF8VyXQV0oFAp2717FH39Mk4LIAEqXdmD69L80/jaVElnhf4EkJCTwPjQU//fvsTQ1xdZSdUJvzsaN3Hv6lJCwMMnzJckTJqWny665c+nesqVKnaOzM49fvMiULKdXr6ZlvXoqdZbffsv7D2kHHKXk6saNNKiumgZ29OLFmBgaYmtpSQkbG0ra2lLC1hZbCwuNROtGRkdz5sYNDp4/z7ErV/gQHp5mO3v7SnTvPpo2bXpjYGCoVhnevXvDxIk/JPfHxtGxFosXH8TWtoRa75URt26dY/jw5gDUrNmE9esv5Or9c4I3bzyZM2cQd+9ekuoEQaBbt1GMGDGPQoWMNCjdR9JT+Hlv6CSTKZ69fs3TV6/w9vPD299f2vyCgvAPDiYwJETKK7J07FjG9+6tcv3lu3f559attLpORVr+8TpZMFFopTGi/VTOk7QwL6zqDx0TG8vqPXvSbKujrU3xxAdA0ja+V69UDzx1Y2hgwPdNm/J906bExsVxwdWVg+fPc/jSJfzfv5faeXk9ZN68ofz++2Q6dRqMs/NwihYtrRYZihQpyYYNl5k/fxjHj28F4NGj2/Tt68TChfupXr2hWu6TGZIrv+joyFy7b05SsmRZ1q07z8GDf7J69UQiIsIQRZHdu1dx5coxpk7dQO3a32pazHSRFX4eRBRFAoKDefLyJU9fvcKuWDGa16mj0mbOxo24nDyZqf4CQkJS1aU00SRHX08Pw0QTS5J5JSUdGjemZsWK6Ghrq246OuhoayMIghS4VaZYaj/x/u3aERIeTmR0NFHR0UTGxCj30dFExcQQFRNDWGQkIWFhWKRQ+MGhn85KqYiP55WvL698faW6n/v2VWkTFhHBzPXrqVK2LFXKlqWSvT2FDNRn9tDT1aVlvXq0rFePtZMnc93dnV1nzrDtxAkiopQmgdDQYLZtW8T27Uv47rtuDB48kzJlHLJ9b319A2bO3EyFCjVZvnwc8fHxBAX5MXRoU2bO3EybNr0z7kQNJH97KSgKH5RBkF26DKNhwzbMmzdUcpP18XnB8OHNGTRoOkOH/ppnk7PJJh0NExgSwt0nT7j35AlPXr2SlHxypdanTRu2zZ6tct0va9Ywf/PmdPs2MzHBxtycHzt0YHL//irnrty7h//795iZmGBqbIyZiYl0rJuHbOYJCQmpIohDw8NxOXmSoA8f8A0M5E3iW84bP79UZiJrc3P8z55Vqbvh7k79AQOkso62NrUqVaJxjRp88/XXNKhWjcLpPBA/l5CwMDYfPcrqvXtTuX9qaWnRpk0fhg2bTZEipdRyP1fXi0ye7ExISCCgnGxctOgg33zTQS39p4e393M6dVK6qRYrVoajRzNn/stPiKLIiRMuLFs2ltDQYKm+bdu+TJu2IdfyBqVEtuHnQXafOcOk1asz5e5Xu1Il/t26VaVuz99/s+noUUrY2FAi0XRR3NqaYtbWWJubY2Vmhp5u3vYXzgkio6MlM9cbPz8SEhL4sYOqgvvz4EGGzpv3yT60tLSo4eBA4xo1+K5OHVo3UO+kXHx8PCeuXmXl7t2cv31b5ZyhoTEjRy6gS5ef1DIX4ev7inHj2uPp+QBQvgH8/vvf1KjRKNt9p0dISCDNmytXnypc2Jzz599ncEX+JTDwHbNm9ZMSxQHUrt2cRYv2ayRKV1b4GiAhIYH/nj/n4p07BIaEMHvYMJXzxy5fpsP48Z+83qhQISqUKUOFMmX42sEhlQ1e5vO5//QpRy5d4oGnJ+6enjx7/fqTbb+tXZt/1q7NMVluPnjAzPXr+fvmTZX66tUbMm3aRrWYeQID3zFwYAN8fLwAZfTohg2XKVeuarb7/hQKRRx16ypHuFpaWty8GVfgUl8nR6FQsGDBTxw+/HGZj3LlqrJy5clPLjaeU8gKPxdIruAv3rnDpbt3JfOCgb4+wefPY5DMFu7j70+JNm3Q19OjWrly1HBwoErZspKSL2ZtnWftgAWNwJAQrty7x+W7d7l09y73PTykhHKzhw1j+qBBKu13nDpFYEgIXb79luI26smnfvnuXYbNn6/i+aSnp8/gwTPVEt3p7f2cgQMbSDl5ihUrw7ZtrjmajqFRI2MpM+XFix/yVTKyz0EURf76ay7r1k2X6mxtS7By5Sm1LfqSGWSFn0O8Cwzk0MWL/PPvv1y6e5egdNwML/35J42//loqi6LIf8+fU6FMmTxlM5dR2tqvublx+e5durVowdcVKqicr96zJ24eHgiCQMPq1enavDndW7bEyswsW/eNiY1l7qZNzN+8GUUy19jy5aszY8YmKlSoka3+PTzcGDy4kbQub+3a37Jq1ekci3No06YE/v7KuYrjx1+pbW4ir3P8+FbmzBlEfLwCUL5RLVlyCCenprlyf1nh5wAfwsOxbdGCmNjYT7axsbCgSc2aNKlZk++bNKGIVf5dvUhGydOXL6nQpUuqej1dXQZ16sQvP/6Y7VG/+7NnDJg9mzuPH0t1urp6/PLLetq375+tvi9dOsr//tdRKg8ZMoshQ2Zmq89P0bVrZby8lDkUd+1yy1ETUl7j5s2zTJr0g/Rw1dXVY/HiQzRs2CbH7y0rfDUQp1CkGom3GzuWE1evSmVrc3NJwTd1cqJCmTIFziwTHhmJj78/70NDCQ4NJTgsDEc7O2qkGAX/+uefXLxzhziFQtqScucnHetoa2Ogp8f4Xr0Y0LGjyvULt2zhuY8P1mZm2FhYYGNujo2FBbYWFthYWGBpapoj6Qoy4kN4OHv+/pu9//zDBVfXVPEE+np6DO/Shcn9+2NjYfHZ91EoFCzfuZMZ69cTnSyCt1ev8YwevShbf/v69bPYsOFXQGk22r37QY4sqjJwYEPc3K4l3vMiNWt+o/Z75GWePr3PmDFtCAxUugjr6emzbNlR6tZtkaP3lQOvPpOwiAi2HDvGpqNHaV6nDovHjFE5/2P79oRGRNDl229pXrs2Fe3sCoyCv+DqytX795Uuj35+vEnc0oognTpgQCqF/9DLi4t37mTqXiFhYanqTl6/zuW7d9NorUQQBKzMzChpa8svP/7ID9/mTsCLqbExQzp3ZkjnzvgFBXHg/Hm2HDvG7UfKJZxjYmNZvnMnfx46xITevZk1dOhn3UdHR4ef+/alU5Mm/DBxIg88leuW7tixjKCgd8yatfWzTTGDB8/g+vVTPHx4i9jYGBYvHsWqVafU/t01Mflo4goLSx0LUtBxcKjO5s03GDq0CW/fviQ2NoYJE75n48ar2TbPfS6ywk+DtwEBrNq9m3UHDkgKzjcoiHkjRqiM8n/49ttcUzTqJDYuDs83b3jk5cXjly+xL16cXq1bq7Q5fPEiq3bvzlR/wWko7KzMSxikEdgV/Ym8NEkkBacFBAcTp1CkOt9m9GiiY2OpUKYMtRwdaVCtGuVKlVKrUrO1tGS4szM/denC3zdvMu2PP3BNVPwRUVH4Bwdn0EPGlCtViuubNtF7+nSOXFKG9J8+vZP4eAVz5mz/rMlcLS0tJk9eS79+tUlISODGjTOcO3eA5s1Tm6qyg4nJx/zxYWHZ/yzyI0WLlmbdugsMHtwYP783REdHMn58e7ZuvaWRhWtkhZ+MR15eLHFxYfupU6mUyIfwcB54eqaawMvrREVHc+/pU249fMithw+59/Qpz968UcmX06ZBg1QKv6StbZr96evpUdzaGiszM8wLF8bcxASnihVTtZvSvz+DOnVCR1sbXR0daUsq62hrE5+QQHRMTJpzG9MHDeKNnx8BiYuZ+AcH4/f+vXScPMDKobRqagJRFLl87x4RUVFccHXlj8R6a3Nz6letSoNq1WhQrRo1K1ZEXy/7wTGCINCyXj1a1K3LscuXmb5uHU9fvWJqsuCu7GBsaMjBxYsZuWgRf+xXphk+e3YvCkUc8+bt/qwAn4oVa9Kly3D27v0dgGXLxlKvXkuMjEzUIjOgsmDIlzjCT6JYsTKsWfM3/fvXJTz8A/7+Pvzvfx35889Las+nlBGyDR944ePD2KVLOXr5cqpz5UqVYmyPHvRs1QozE/X9GHKaE1evMv2PP3D39Mxw2T+74sXxOnJEpe6GuztHLl2SEpOVLFKEkra2WJmZ5QmzVZxCQUBwMF4+PjhVrKjyluDt50fJtm0z7ENfT49ajo64zJ6dZvqHzyUhIQH3Z8+o7qDqQx8aHk6PqVNZMnYsFe3sstyvKIqMW7aMlbt2SXWNGrVj8eKDnzXSDwsLoUuXCpKrZq9e4xk3bmmW+/kU69fPZMMGZYT44MEzGDr0V7X1nR+5efMsY8a0ln6PzZs7M3/+HrX/nmQbfjqsP3CA/61YIeU4SaJe1ar83KcPHRo31sjkYFaIiY1NNVItpK/PvadPP3lN6aJFcbSzo6KdHZXs7VOdr1e1KvWq5l2vCl0dHYolRhanpKiVFU8PHODpq1c88PTkurs7193dU+XgiYmN5c7jxxRRc2I1LS2tVMoeYNLq1Zy8do2r9++za+5c2jTMWjIzQRBYPn48ujo6LEkcCF25cpw1a35hzJjFWZbTxMSMsWOXMn26Mqhv9+5V9O79P7WZGpKbdJKnHvhSqVv3OyZMWMXChSMA+OeffdSu3ZzOnYfkmgxfvML38vGRlL0gCHT85ht+7tOH+tWqaViy9ImMjub4lSvsOnOGK/fu8frECQyTJQBrkIH8SQnGTl2/jrmhYSovmfyMtrY25UuXpnzp0rRv3BhQjrqfvHzJNTc3afN884ZvatZMNYfg+ugRfx46xI/t21O3SpVUIzCLxo0Jjsw4IZi5oSHvE98a3wUGsu3ECQBCIyJoN24cC0aO5Oe+fbM0whMEgUWjR6OtpcXCxHQbLi5LqFatIU2aZP1/2KpVT/btW4O7+w3i4xXs3/8HP/00J8v9pEV2TTqBgb4MHNaUv9ZdxMqqiFpk0jTOzsPx8nrIvn3K6O3ly8dTu/a3lCjxVa7cv+DGOmeSX4cOxdHenop2dtzcvJlDS5bkWWUfp1Bw8upV+kyfjm2LFnSbMoXDFy8S9OEDx1KYo5JG/CGAmMGWGeWV39HS0sLR3p7B33/PllmzeHboEO/OnGHl//6Xqu2GQ4fYcOgQ9QcMwNHZmUVbt6osfB4cGZnhZ5rycy1iZcW1v/6iVBGl4hJFkUmrV9Nnxgyi0kg/nR6CIDBvxAjaJntD+PXX/vj4ZD1BmSAI9Oz5McXHwYPriYnJmjyfwtg4e146G10W8faDPxtdFqlFnrzC2LFLsbd3BCAqKoKZM/tlaHZVF2pR+IIgtBIE4akgCJ6CIExO47y+IAh7Es//KwhCGXXcN6vEKRSERUSo1Bno63N8+XLubt9O7cq5F/6cFR6/eMGoRYso2rIlbceOZfupU4SnUNL//vdfmtfmfuqm/IOtpSXlU0z4RkVHs/vvj0mwnrx8yaTVq7Hr0IGfV64kIBueN9UdHLi9bRuNanx0ydtx6hTfDBmCj79/lvrS0tJi66xZ0gMkLCyEKVO6EhubvndTWjRp0kmKgg0ODuDMmV0ZXJE5VN0ys/a5BQb6cuzEVsRz5zh2YiuBgRknGcwv6Osb8Ouv29DWVhpY3Nyu4eKyJFfunW2FLwiCNrAGaA04Aj0EQXBM0WwgECyKYllgObAwu/fNKi98fGg0aBD9Z80i5US1XfHiaboGapoLrq58N3w4js7O/L53b6rUDeVLlWLm4ME82b+fZekkYpPJPAb6+pxcuZKBHTtibPjRgyIqJoYlLi7YJWbe/Fy1b2NhwT9r1zL4+++lutuPHlGrb19ufeKh/SkszczYu2CB5AL76JErK1ZMyLJMOjo6ODuPkMq7d69K9Rv5HLJj0tnosoiEvv2gRg0S+vYtcKP8ihVrMnjwDKm8bt10nj1zz/H7qmOEXxvwFEXRSxTFWGA3kNKY2BFIyu+7H/hWyEVXj8cvXlCzTx/+/e8/Dl64wLoDB3Lr1tni8YsXqValKm5jw4Q+fbizfTtPDhxg1tChOJQpoxkBCyCCINCgenU2Tp/OuzNn2DprFjWSTcAmzfeUA3agNN1kFT1dXdb/8gu/T5woOQT4BgbSeMgQzqbImpkRdSpXZsnYsVJ5797fuXjxcJZl6tRpEPr6hQDw8LjPvXtXstxHSpKP8MPDM6/wk0b3cZMnAhA3eWKBG+UD9O8/hUqVagPK7KIzZ/ZFkUZMiTpRh8IvDrxJVvZOrEuzjSiKCuADkKZrhCAIQwRBcBUEwTU7r89JhISF0fF//5M8NHS0tYmNi8t2v7lBnzZtMDEyQktLi05NmnB2zRpeHz/O4jFj+LpChTzhHlmQMSpUiL7t2nFn+3YOLl5M5a8+TqwFAdOAz7V2C4LAiK5d+fv337EwVRreYmJjGTJvXpqBZOkxqls3fmjWTCovWTKG6OiodK5IjampBW3bflwZ7NSp7Vm6Pi2S2/Cz4qUjje6LFlVWFC1aIEf5Ojo6/PrrNmmReQ8PN2lpypwi2374giA4Ay1FURyUWO4D1BZFcVSyNg8T23gnlp8ntglKq88ksuuHHx8fT7tx4zh9/TqgXHf07Jo1eXJS9oKrKxXKlKFoiiCko5cuUa18eUonffmzgODklKkRqACIrq5Z7j+ziKLI+w8feBcUxIfw8FSfv19QENtPnUJLENDS0kIQBLQEAT1dXYpaWUmLvGg6BiAhIYG9Z8/SY+pUAM4CzdNpn9nP9bm3N02HDsXU2JjDS5fyVYmsLzgeEhZG+c6dpTmGMWMW06dP1sw79+9fY9Ag5USwmZkVp0/7ZiuTpiiK1KmjI+UbunkzNsN4gcBAXzp2r0TMo4cfFT6Ary/6jpU5svthgfHYSWLjxjmsW6c071hbF+PQoWfZCsjKaT98b6BksnIJ4O0n2ngLgqCDci4xx5fA+WXNGknZA2yZNSvPKfuo6Gh+XrmSNfv20eXbb9m3UHV6o8M3+SPhVHx8PE9evuTukyfcffIET29v3gUF8S4oCL+gIGnUWtjIiA+JKQKS8Pb3Z8KKFRneQ19Pj1JFivBk/36VxTSSFEpOL7ChpaVF95Yt6TF1KjtIW9knkPXX5q9KlOCftWspZm2tMm+QFcxMTJg5eDAjFylHwZs3z6NTp0EqZpWMqFq1HjY2xfH39yEkJJA7dy5Sp056j7T0EQQBExMzPnxQ/tTDwkIwN08dN5GcVKP7JJKN8iePW/bZMuVFevUaz759Z490yAAAIABJREFUawkKekdAwFt27lzBgAG/5Mi91PELuQ2UEwTBThAEPaA7cDRFm6NAv8TjLsB5MYdDfHedPs2ibduk8tQBA3Bu/vlf3pzg8YsX1OnfnzX79gGw/9w5jqZQhvmBR15emDRuTOVu3eg7cyYrdu3i+JUruD56hLefn4qJIjQigsgUboiZ/SrExMYSFROTSrHf9/DA8ttvaTVqFLM3bODszZuEppHkTZ30TKPuIuAEeH1Gf+VLl/5sZZ/E4O+/x7640pqatEh6VtDS0qJZsx+k8vnz2Z/ryoprZkrbfUoKqi2/UCEjhg37uGb19u1LCA8PTeeKzyfbCj/RJj8SOAM8BvaKovhQEITZgiAkLSb6F2ApCIInMB5I5bqpTu49ecLAOR+DR9o1apRqiUFN43LiBE59+khZEAE6fvMNtSpV0qBUnyY6JoZNR47QZvToVH7jdsWKEZuB3dnU2BiH0qX55uuvU0U121hYMK5nT8b26MHo7t0Z1a0bI5ydGdChA63q16eSvT2miYuKl0gj1/wNd3dCwsI4c+MGM9evp8XIkZg1bUqVbt34af58rt2/n82/PmO8UI5k7qH0Ysj+lKdy+cPkb6gZoaery28//SSVd+5cQUBAypft9Pn2248J1C5cOJht//DkbxgZ2fE/ObpPooDa8gHat/9RCr4KDQ1mz57VOXIftUTaiqJ4EjiZom5GsuNowFkd98qELAyZN4+oxGyLDqVLs33OnDy1nubp69fpO/PjohP6enqsGD+eoT/8oFYbtbmhIUImI0I/RWBICH/s38/ve/fi/175an7p7l1a1a8vtSlkYICjnR3vQ0P52sGBrytUoPJXX1HcxoYilpbYWlqqRAGnpFSRIplyKw2LiCAsjb/H/dmzVHVJK4r99/w56w4coH7Vqkzs25f2jRtn+7uQ0ecaBDQGTLLh6rvx8GFGLFyIiaEhzw4dwrxw5pYH7NaiBYtdXLj39CkxMVG4uCxh/PjMm0CqVq2PpWURgoLe8f69P25u1/j668af+2ekcM1MX+FfunSEOO8XsOrT5r044FIJuwJn1tHR0WHAgKnMnq1MuLdr1wr69v35sxLjpUeBS57m/uwZ1Xr0AJQ+1fd27KBCHnJbfPPuHTV69ZJ86iva2bFn/nyqlC2rYclU8Xj1iuU7d7L1+HHp4ZnEqG7dWPXzzyp1EVFRGBUqlJsiSoiiiJePDzfc3bnx4AE33N1xe/Ys1eIkAMOdnVkzaVKOyHHD3Z3vf/5Zisq1Njfn2l9/Ua5U1pb2C4+MpKKzM95+yqRmY3r0YEUaEcGf4uTVq7RNdNU0NbXg5EkfyRMkM8ydO4RDhzYAMHjwTIYOnZV54VMwcWIXyTQ0b95uWrTo9tl9FXQUijg6dvwKPz+l0+PixQdp2vT7DK5KTXqTtnln2KsmqpYrh/vu3Qzq1IlBHTvmKWUfp1DQ/ZdfJGVf1MqKi+vX5yllf+XePTqOH0+FLl1Yd+CAirIvbmPDwlGjmNi3b6rrNKXsQTk5+FWJEvRu04Y1kyZxd8cOPly8yLk//mBgx44qufl7tmyZY3LUq1qVC+vWSW6WAcHBtBw1ineBgVnqx9jQkOXjxknl3/fu5ZFX5mcGWtWvL2X//PDhPRcuHMrS/Z2cPrp43rlzIUvXpiT5CD80NMf9NPI1Ojq6tGvXTyofO7ZF7fcocAofoErZsmyYNo3VE9Oe/NEUU9es4bq7MppOW1ub3fPmZWsZPHXi4++P86RJNB48mKOXL6tMpNZwcGD7nDm8OHqUif36UeITufLzEsaGhjSrVYuN06fz8tgxJvbtS+v69WlQvbpKu6joaKatXZvmqlufQ0U7O44vX06hRHPOCx8fWo8eneVJ5B++/ZZvEhe9j4+PZ9yyZZme3NbS0mJghw5S+fDhDVm6d/LFtt3dbxAd/fm5luSMmVkjucK/du2ElLpaXRRIhZ8Xef/hA1uOH5fKc3/6icaJP+i8wNuAAA6cP69S17ZhQ86vW8ed7dvp1bp1llaxyksUs7Zm4ejRnFi5MtW5LcePM3fTJhydnTl88aJa7levalX2LlggRdHe9/Dg+59/TnfB+5QIgsDKCROk+Ya/b97k+JXMTwX/2KGDdK2r6wXevPHM4IqPWFraSsm9FIo43NwyP3GckqzY8GWgZMmyVK+ujIWIj4/n9Omdau1fVvi5hIWpKfd27KBh9eq0bdiQn9Mwi2iSWpUqMbRzZwB6tW7No337OL5iBU2dnApMRG/KvyM+Pl7KK+8bGMj3EyYwaM4ctYS3t2vUiPVTpkjl87dvM/i337LUR7Xy5RmSLOfO+OXLMx0lXtzGhjYNGkjlI0f+ytK9a9b8OMp3df18s448ws86yUf5x49vUWvfBUbhvwsM5IKrK17e3nk2dUJxGxvOr1vHzt9+y1NeQ0nMGzGCi+vXs33OnM9akSm/IQgC80eOxDbZAih/HTlC559/znLK4rQY2KmTipukx+vXaS4Cnx6zh/2/vfMOayLr4vDv0pUiKqhgAVFQsWDBhl1RsXdUxF7XLjbsrnXVxYJlLaifFdC1V0RFXbvYEFEU7ICAqCgdwv3+SJhMIEB6Asz7PHkyM7lz72FIztw595RJTKW1iM+fcSpIcuU7vm9fZjsw8JhUCdHYZp3nz+9IfF5uypQRmiw5hS8Zzs6uTF6jt29D8OWLLJEd4tE8rSMjVx8+RMdJk1Cjb1+4L1mibnHyRVdHByYCn3J1cv3Rozw3xrImJmjXpImaJFI9WlpacO3cGWHHjmEoazH33H//oeu0aQqx6y8cMwYLRo/G8b/+wr39+5l4AkkxL1sWMwVeZwCYID1JcHFyYoK5oqLe4dOnvO6r+ZGT1AsAwsOfivV4kgQTE6HCT0wsMJMKhwAjIxM0aSKMsJfnCSs3xUbhf/oqjL7LyRHOIZ4bwcHoMnUqOkyciOj4eHWLo3bKlSmDI6tWYf5I4aP0f0+fot2ECYiR0sMmNznFSgY6O8tsGpvQvz90BOsB/z19KhKsVxB6urro1LQps3/37iWJx6xYsQpMTfl5nZKTfyE6WvriKgBQpozw6YlT+JLDNqnJ6ynFptgo/I8xMcy2lQYp/AehofA6fBgvIyMVkmNcXqLi4jB44ULweDzcDQnBlHUqL02gkRBC8Ne0aSKphkPevkXrsWMR+eWLGiXju+/2F2TDtLKwwOevkqcW6MYKkJNG4RNCUKuWsFDL69dPJT6XTc5NA+AUvjSwTWqPH99QmO4oNgr/U6zQfUmTZvi+AQGYs3kz6g0ejOW7d6tVlozMTAycP5+JmK1Qrhy2aZjrqrqZ7e6OA8uXMx4276Ki0H36dIXmKc/KysLzN2+kOmfx2LG49s8/eHfmjFTFz7uxFm4fP74hlYtl7dpCL7Lw8CcSn8fG1FR0hq8Jk56iQK1ajWBoyI+ujouLksrLqiCKj8LXUJPO9UePmG2nBg3UKAmw++RJ3H/xAgA/DsB/zRpUFpObpqQzomdPnNqwAQb6+tDX08PuRYvkShOcA6UUF27fRkM3N7QZPz5PmcqCqF+zJjo2bSr1Yn+1SpVgb2MDAMjISMfjx5In5xOd4cum8A0MSjNRvpmZGUhNTS7kDA6An2qBndLi8eMbCum32Ch8tnU0W4NmEWyvDFnynCuSewJlD/Czh7Z3FBt9zQGgV9u2CNy+HcfWrlXYQjaPx4PHxo14+e4dficnK8zvvzA6N2/ObIeGSl5Ry85OmEr8/ftXMo/PNuv8/CnfmkhJwsFB+HQWGSld+cv8KDYKPyctLMB/DNcUKrFc/nJMKeriM8vs1SZXxClHXlo3bKjQegQ6OjoYw4qAPXxJcpu6PDRjZWB9+fJRAS1FqVzZhjFtff36SeaI23LlhJHZ378rNnK0OFOtmh2zzZl0cmHDmj2re5GNDVvhy+vxIS9shV+1CKRHKI64ubgw24EPHkj9nXgfFYU9p05h8IIFeCfh99yxTh1m+9WrYInt6Lq6erC0FMZjSOPWyaZsWaHZ8Pv3OJn6KIlUrSrMsfX5s2zXPjfFRuHXKAIz/K8J6vNSyM7ORlSc8MdWVYPWOYoK1x89QuuxYyVWtOKoWqkS2gtMRNnZ2fALCJDq/Ol//40Jq1fjWGAgAh88kOicmlWrMv7/P37E4+vXTxKPZ2UlLOD+8WO4VLLmUL48N8OXhZz8+AAQFfVeIY4DxUbha+wMn1WjVp0KP/7HD6byVFkTkwLz03PkZb63Nzr98QfuPH+Opbt2ydWXe7duzLa0Zh3nZsKAqKsPH0p0jpaWFpqwZvlhYZLXLxZV+NJ5FuXAzfBlo1QpQ5iZ8YvB8HhZ+Pr1o9x9FhuFX1Og8OvVqIFBnTqpWRohFqwZ/jMpXfEUyQ9W1CghhHOPk5LebYUeE74BAXJF4Q7o1An6evzCFk9ev5bKr54dSHXrqeS+8WyzTni45OexFf6nT7J9f9k2/ISE4lWeUNmwZ/nR0R/k7q/YKHzbatXw7OhRhPj5YdLAgYWfoCI6sn6gl+7eVZsdv7qlJZra22PlpEl4cvhwsUmIpipaNWwIR3t+Bsns7GzcfPxY5r5MjY3Rsn59Zj/4leQeMPY2NszTWdz37/j2s+A6sezzcpBGcVeuLDwvJuaDxOexMTe3ZLbj4jTH3FoUYJeIVIRLa7FR+FpaWnCws9M4RWZnZcWkQebxePjfuXNqkUNfTw8PDx7E4nHjYJVfzVCOAmHPrq8HS24WEUfOzQMAHoWFSXyelpaWSGK7l5GREp1nW7Uqsy3N4quFhRWzLesMs2JFobk1Lk5zzK1FgZwkagCQnp5aQEvJkEvhE0LKEUICCSFvBe9l82nHI4Q8E7zOyjNmUWR8376oV6MGtsyZw6Qg5ih6sJ/W2AF1stCUpfCDpVD4AFCXNVt/KWElrJoshf/lS4TEJr1KlYTlGePjo5CVJX0m2goVOIUvKxql8AF4ArhGKbUFcE2wL45USmlDwat3Pm0UjjSRjMrEzcUFIX5+mD5kCFP+jqPo0crBgSkCExoZydSulYUcm7qVhYXUkeGyKHzzsmVhYmgIAEhJSZK4kpKenj5jksnOzkZsrPQKm23SSUj4qtA0FcUdTVP4fQAcEGwfANC3gLYqITEpCZuPHkUTd3c4T56sEYuTWlpaGmdqivj8Gd5+fuDxeOoWpchgWKoUWrLSYwTJYdapXrkyYq9cwYdz5+AjZTpvtj1eUpMOIUSkmLo0ft0WFtbMtixZM3V19RjXzOzsbHz7FlPIGRw5sIvPp6fLX6NBXoVfkVIaAwCC9/wSsxgQQoIJIfcJIQXeFAghEwRtg+N/SF8wIT0jA3O3bMGT16/xIDQUJ65dk7oPVaCuG1FmVhY6TJwI2379MOPvv7F63z61yFFUYZti3kdHy9wPIUTmesa1rIR29Y9SePiwo9FjYiR38WPb8WNjP0t8HpuKFYUmJUW4F5YU2HpCETqjUIVPCLlKCAkV8+ojxTjVKKWOANwAbCaE1MivIaV0N6XUkVLqaF5W7JJAgVQoVw5TXF2Z/bne3khLT5e6H2UScO8eHIcPhyw3NHnR1dFhimMDwJ979sjlcVLSMBC4UwJQ29MRO5gvNkHyDJSW5ubMtjSzbLYNXhaTDgBUriz8ySsqTUBJgJ1Smp15VFYKVfiUUmdKaT0xrzMAYgkhFgAgeBcbVUEpjRa8vwNwA0Ajce0UxbLx41FeYCv/EB2NbceOKXM4qVi0fTtcpk3Dk9evMW39erXIsHjsWMZzKDs7G26LF0vs3lfSycktAwBZalL4RqVLY9rgwfhz4kRs8vCQWOGzY0KkUfhsL5v4eNncKtn+5F++SGaG4hBV+OxiMrIir0nnLICcMkEjAZzJ3YAQUpYQoi/YNgPQCoB0bglSUtbEBMsnTGD2V+/bh++JicocUmJas5KW+QcGqsXkpKOjg6OrVjE3xej4eIxavlwj1js0nV5t2mDnggXwWbxY7sRq76OicPL6dew5dQq3nz2T+DxCCLznzsXS8eMxccAAiVMmq3OGz1b43Axfcn7+1CyF/xeAzoSQtwA6C/ZBCHEkhPgI2tQBEEwIeQ4gCMBflFKlKnyAXxYuxxXt5+/fWLN/v7KHlIhurVphdK9ezP7kdevUYtqpXKECDvz5J7N/4fZt/H3okMrlKGo42ttj4oABGNu3LxrXri1XX6dv3MCAefOY3DjKxoKV5kNWhS+rWyU7ERg3w5ccjZrhU0oTKKWdKKW2gvfvguPBlNJxgu27lNL6lFIHwfteuaWWAD1dXaydMoXZ3+zri7vPn6ti6ELZ6OHBzLbivn9H39mzkZom/wq8tPRo3Roew4Yx+/O8vfHPv/+qXI6SCttF9/uvX0ofT1ThS77gzDbpyLpoKzrDf8s9TUoApRQ/fwprTqtd4Ws6Azp1QptG/OUCHo+HIQsXIkEDbNWmxsbYs2gRs383JARDFy1Si3/y2qlT0YIV5j/5r7+w6cgRlctREsnOzma2dVhrA8rCzFQYpv/rl+RPleXKVWTWLn7+/IaMDOmdIMzNLWFoaAwA+P37J+LjZfdwKinExn5GcjI/Z5OxsSnKlJHNq4tNsVb4hBAcXrkSZU34tSE/x8Zi9J9/asTsonvr1tjk4cHsn7l5E1PWrVO5bHq6urjk7Y3m9eoxxxbu2CFVQq+ShCKL2CSnCgNpDEuVKqBlXjYePgyXadPQ6Y8/EHDvnkTnmBobM9u/f/+Q+Lumra2N8uWFwWGy+NETQlCzpjCG4e3bEKn7KGmEhwvXdezsGioklqdYK3yAX9Pzf8uWAeB/4cf06aMxQVAz3dwwd/hwZn/3qVNYsWePyuUwNTbGlW3b0LphQ2hpacF39WouX74YvsTGomKXLrDp0wcTVq+Wu78ktsKXMl31i8hIBNy7h+uPHuFLrGRRs/p6eiilrw+A/8QrTTIuc3OhD7+sCdBsbYUKPyLiRQEtOQDgzRtRha8Iir3CB4De7dph27x5eHrkCPq2b69ucUT4a9o0kfzoq/buxdtPkheoUBQmRka4vHUrzm7cqHHXSFPIqQn8PipKIf8jeWb4eqyi6plSmAJznnYB6cw6FSoIFb6srpnsGX5EBDfDLwy2wq9Vi1P4UjHF1RXWlpaFN1QxWlpa2Lt0Kbq0aIHSBgY4u3GjSAi8KjEsVQo9WrfOc/xBaKhabkKaxr0QoZJip1iQFXkUvq6MCp9t1klKknw9S9EzfM6kUzjsugXcDF8BvHr/XiMCjvR0dXFi/Xrc9vFBt1atCj9BhcT/+IH+c+eisbs7jl6+rG5x1EZaejqOssoRtnJwkLtPtjtuTglCSWH73vNYi7+FYcoa5/dvyb/77Bm+rK6ZNWrUY8yp79+HKSS/e3ElKuo9k45aT08f1avXKfgECSmxCj84LAxtxo1Dt+nT8SspSd3iwKh0aTQS49f9JTZWxJtD1UxcswbR8fFISknBsMWLMebPP0VmpiWFgxcuMNkxq1SsiM7Nm8vdZ/hHYU4ZOymf6lJZ6UKkKVfJbitN9kVF+OIbGZnAxqYuAP4aQmioZDV5SyI3bwpjWJs27QRdXb0CWktOiVT43xMT4Tx5MhISExEcFobeHh4al28HAD59/YoWo0dj6KJFSM/IUIsMi8aMEcmlvv/cOTgOH47naizXqGp4PB42sILSZg0dCj1dXbn6pJTiNUvhsxOiSUIKK24jZyFWEtht09JkU/iyRtsCQMOGQpPh8+d3ZO6nuMNW+O3aSZO2rGBKpMIvV6YM1k+fzuzffPIEgxcs0Kg83T9//0bXqVMRFReHY4GB6D5jhlqeRJrUqYMnhw9jGGth+fWHD2g6YgSW79qlthuRKjkVFISIz/yAI1NjY4zv10/uPjMyMzGuTx90c3KCg50dKpaXLqhGETP8tDTJ60VUqiS86csafAUADg5Ck+WzZ7dl7qc48/NnAp4+vQWA787atq3iSoiUSIUP8FMvrJs2jdk/e+sWxq1apVbzCRsTQ0M4N2vG7F9/9AjtJkxQS01cY0NDHFqxAv9bvpxRGJlZWfhzzx40cXfHHSnywBQ1KKVYf/Agsz9l0CAYCwqJyIO+nh42enjgorc3nh09KrWrMHuGrwqFz160/fYtWuZMoewZ/osX97h6DGK4ffs8o4fq128BMzPFuUiXWIUPAPNGjhTxgz9w/jyGLV6sEbNWLS0teM+di9WTJzPHnr15g8bDhkmVaEtREEIwsmdPPDl8GE4sD5WX796h9bhxmO/trXKZVIFfQABTc1ZfTw/TBg9Ws0R8ZDbpyKjw9fUNYGrKT83A4/GQkCBbYJ6FhRVTASs5+beI6yEHnytX/Jnttm0VZ84BSrjCB4B106djbB/hRfW7ckVt5pPcEEKwcMwY7F2yhAlt/5qQgA4TJ2Lz0aNqiRiuZW2NW3v2wHvOHBFXQkW4KWoi7IXV8X37Sm16URYJrOyv0pTN1GetPWRlSTexYde3laaAChtCCJo0ac/s37lzSaZ+iivv37/C3bv8a0IIgbPzQIX2X+IVPiEEuxYuxJRBg5hj6jSfiGNMnz4I2LqVyYWSxeNh1saNGLpwoVrq9mpra2PakCF4eewYBjk7o2PTpuiTK1VwVlaWxpjH5GHZhAmYNngwalSpglWspy15UMSNeueCBTiyahW8Zs6ElYWFxOex/felLUhuaVmd2Zal1GEOrVv3YLZv3z4vcz/FkSNHNjLbbdv2Fkk6pwhKvMIH+Aps67x5ecwnJ69fV6NUonRq1gxPDh9Gs7p1mWP+gYFoPmqU2oq1W1lY4Nhff+HC5s15bNCHLl6E/aBB2H/2LDIypVMsmgQhBJtnz8aD//1Pal/5/HgaHg7bfv0wac0aXLojm6dKm0aN4ObiAg93d6nkkk/hWzPbOT7istCypQsTRxAa+gBRUbLfPIoTCQmxuHhR6A3m7j5b4WNwCl9Ajvlk39Kl/Bns4MGYzJr1awJVK1XCrT17MGnAAOZY5+bNYVS6tBqlAgxy2ZCzs7Ox7sABhH/8iDErVqBm377Y4uur8f77lFIcunAhzw1KS0sL5VmZJuUl8P59RHz+jF0nT+KIioPZ2Fk5eTzpvNIUNcMvU6YcWrTowuyfOaOSjOkaz/Hj25lMpHXrNhNZ4FYUnMLPxejevXFv3z5s8vDQmCRrbPT19PDPggXYv2wZOjdvjg0zZqhbpDy8/vBBxBz2OTYWM728UK1nTyzcvl3iZF+q5HdyMsasWIERy5ZhxNKlSvUeufrwIbPN9sRSBYqa4cfEfJBLjn79hBXpzp7dJ7UsxY3U1GQcP76D2Xd3n60U/cMpfDE0rVtXpHYpACSlpGDH8eMaY5ce1asXArZtE/kBA/xyhS8i1FtCzt7GBp8uXMDaqVNRoZwwh/f3xESs3b8f1Xv3xtCFC3EjOFgjYh/uPHsGBzc3/O/cOQB8U5myCsHE//iBm0+eMPudmjaVuo/MrCyZ1wEUZcOXt2pVmzY9Ub58RQD8dMuXLpXsGgzbti1gqltZWlqjQ4f+ShmHU/gSQCnFqOXLMWXdOgyaP19tNvPc5J4BUEoxduVKOA4fjg0HD6rVx7mMkRE8R43Ch7NnscPTE9UrC/24s3g8+F25gg6TJqEzqyqZqnkZGYlZXl5oO2EC3kcJE4K5d+uGCf3z/8GVa9sWxNGx0Fe5tm3znHvk0iUm2VnzevVkSkO91c8PJu3aoZGbG3xOn5bqXHYOHmlvGpUr2zDfuZiYj0hPl71Km46OLlxdhXEwPj4r89yAvn2LQZ+BtfHtW/GuzfD48U34+29l9idMWA6dXBM5RcEpfAk4dOECTggWcE8GBaHl6NF490X28HJlsfvkSVy+excZmZmY5+2N9hMnql3OUgYG+GPgQLw9eRKn/v4b7Ro3Fvk89z4AvIiIUEpSO0opQt6+xdKdO2E/aBDqDR6Mzb6+zFNbGSMjHF65EgdXrCgwdcKPlBRQoNDXj1wTA0op9p4Rhsyz3YGl4e3nz0hKScGzN2/wO1m6BGTsSYK0T6v6+gaMWYdSKncx8sGDpzFVnKKi3uHixcMin/scWo/oxDj4HFov1ziaTEpKElasGM3st27dAz16jFDaeHIpfELIIELIS0JINiHEsYB2LoSQcEJIBCHEU54x1YGbiwtmDB3K7IdGRqLpyJG4xrLFagJtGzdGkzrCrHq3BaYKn9On1V7lS1tbG33bt8eN3bvx9MgR/DFwICzNzcXm3h+3ciUqdumCtuPHY8bff+Off//FjeBgfP32Ta6/wzcgAA5Dh2Kljw9evRdddOzYtCle+PlhWLduSlu7CQ4LQ2gk3xRS2sAAgzt3lqmfN6xU1dKm0tZi/W2yXEsrq1rM9seP4VKfz8bIyATDhgk9UfbuFc7yv32LwbkLB0CvXcO5CweK7Sx/61ZPxkvJ2NgUixbtVuraobzPDaEA+gPYlV8DQog2gO0AOgP4AuARIeQspTRMzrFVho6ODjbPng0HW1tMWrsWGZmZ+J6YiK7TpsFr5kxMHzJEIxZ461Svjnv792PNvn1YuXcveDweklJSMH7VKpy+cQN7Fi8WKWStLhrWqoUdnp7YNm9enusWFReHhy9fAgD+e/oU/z19KvJ5GSMj1La2Rm1ra1SpUAEmhobQ0tJCUkoKklJTRd4Xjh6NZqzSjV1atIC2tjZj6iptYICebdpgcOfO6Nu+vYi5QxnsO3uW2R7k7AwTGd082bUJbFmJ7SSBiCh86dejrKxq4e5dvmeRvAof4M/yjxzxQmLid0RFvcf58wfRt+9Y+Bxaj+wRI4FGjZA9YgR8Dq2H56yNhXdYhHj06DqOH9/O7M+Z481EISsLuRQ+pfQVkNeWnItmACIope8Ebf0A9AFQZBR+DqN790ad6tXRf+5cxHz7Bh6Ph5leXrj97Bn2LF4sUlxCXejq6GDZhAno0bo1hi9ditcfPgAALty+DftBg7Bx1iyM6tVLI25Q4hTaFupfAAAgAElEQVRs/I8fcGrQAPdevBA7A01MSsKD0FA8CA0ttH83FxcRhW9maopebdrAQE8Pg5yd4eLkJFUeGnn4lZQkUk9gTG/ZEmKlpqXhs8DLSUtLS2RtRBKIAmf4Hz68lvr83BgaGsPdfQ62b18IANiz5080adIO5y4cQGYY/8af6TkP5+zrYdzweQrNK6NOYmO/YNkyoemmTZte6N7dXenjqsKGXxkAO73eF8ExsRBCJhBCggkhwewCEZpCi/r1EXzokEjR73+vXUNjd3c8EsxMNQFHe3s8OXwYM1mmqJ+/f2PMihXoPGWKVFWSVEnDWrVwZ98+xFy+jJMbNmDt1KkY0aMHmtWtC5NcScvY5frEIW5x/eSGDfBdswb9O3ZUmbIHgK3+/vglsLfbVquGNo0aydQP2wOrRpUqUqdpllfhW1sLTYaRkYXfdCXB1XUqk6cnNvYz5noO4s/ucyKILSyYWX5xICkpEdOnd2Mqh5mYlMWiRbtUMgkrdIZPCLkKQNxtdRGl9IyY43m6EHMs328apXQ3gN0A4Ghvr17Dcz5Ympvjxq5dmLVxI3aeOAGAX+d0w6FDOPbXX2qWTkgpAwNsmj0bvdu1w7iVK/FO4IlSo3LlPO6cmkbF8uXRr0MHkWOUUnxNSMDrDx/w+sMHZGZlIezdO5Q2MICxoSGMSpWCUenSzHtjMQVl1PVk075JE3RwdERQcDAWjh4tsxyPXwtn1Y51pK+CxF6olcWEZWcnrPQVEfECaWmpMDCQrjxjbgwNjTFr1kZmxhsR/gzYsEKkTXGZ5WdkpGPOnH7MzVJbWwdr1x6DmZnk6THkodBfPaXUWc4xvgBgGxqrAIiWs0+1Y6Cvj38WLEAHR0eMX7UKpsbG2LVwobrFEksHR0e88PfH0p07cfTyZaydOlXdIskEIQQWZmawMDNDB8d8fQQ0klYNG+L6zp248+yZyNOhtASHCS2hTWRQ+OxZvSw3HROTsqhWzQ6fPr0Bj5eF8PCncHBwkrqf3HTv7o7Ll4/g3j1BGUlPT6BLFyAnips1yy+qtvzs7Gz8+edoBAcHMceWLt2H5s3lVbGSowqTziMAtoSQ6oQQPQBDAJwt5Jwig2vnznh29ChObtiQx8Sgbs8YNqUNDPD3zJl4c/JknuyK337+xKjlyzUyAra40aphQ7l8rNkz/CZinmAKQ/Q7KdtTRr16wvKOiipTSAjBpEkrhQfCwoA1a0TaZHrOK9IeO9u3L0RAgC+zP3nyavToMbyAMxSPvG6Z/QghXwC0BHCBEBIgOG5JCLkIAJTSLABTAQQAeAXgGKVUc4zdCqB65cpiZ1seGzdi8Y4dGmUvF5d3Z763Nw6cP4/aAwdiw8GDRTrZmaahyJt+aloa49YJQGwN5ELlYW3LalZShsIHgHNXjkKrXXvhgbVrgZAQ4X4RteVTSrF//1ocOLCOOda//0SMHr1A5bLIpfAppacopVUopfqU0oqU0q6C49GU0u6sdhcppXaU0hqU0tXyCl0UOHvzJjb7+mL1vn1oOXp0Hr9vTSHi82fsF6QUSE5NxTxvb9QfPBinb9zQqCcUTaJs6dIgQKEvfR0dTFi9WmE30Kj4eMat1rZaNZmyd8pr0gFEFX5IyB2FfU9u3jyD7Js3hAcyMwEHB4AQ5pXpvRk3b0oXXaxOfvyIx8yZPRkvJICfVmLevG1qWU/iIm2VxKGLF5ntx69eobG7O7awojo1hZpVq+Lm7t2oa2PDHHvz6RP6zZmD1mPHFuvyhbLy/dYt0ODgAl87PD2RnpUFn9On4Tx5stQRseKoWbUqPp0/j/ATJ2ReL5J30RbgL9waGvJdkGNjv+DjR8UUtL90+h2CgymOHw+DgYHwSdTGpi5On45AcDBFcDDFpdPvFDKesgkODsLQoQ64c0eoCxo0cMKaNX5KS51QGJzCVxL+a9fi75kzGbe5tPR0zPTygvPkyfgYE6Nm6URp06gRnh49io2zZom4Pt4NCUHrcePQx8MDYe+Kxo9MEwgKDsa0DRuY/crm5gpLYU0IgZ2VlcyL1tms2biWlnYBLfNHR0cXTZoIPagePAiUqZ/8qF69DhYv9mH23717iZEjm+Hhw2sKHUdZZGVlYceOxfjjj0749k34W3d3n41du4JQqpT8NZFlhVP4SkJLSwuz3d0RfOgQHOzsmONBwcGoP2QI9p89q1EmE10dHcwaNgwRp09jxtChIm6bZ2/dQv0hQ7DKx6eAHjgAIPLLFwycP5+J5m1Spw72Ll2qEYFugGJm+ADQvLkwLcTDh1flkkkcLi5DsXz5Aejp8b10EhO/Y9q0rvD13aJRv5vcxMR8xIQJ7bBv32pGzrJlzbFly0XMnPk3dHX11Cofp/CVTP2aNfHwwAEsHD2a+YHl5F7vO3s2YhMS1CyhKOZly2Lz7NkIP3ECw7p1Y45nZ2ejga2tGiXTfH4lJaHXrFn4Lqg3a2FmhjNeXioN8CoMHkvhE6IYhR8cHKSUNNc9e47A7t03GR91Ho8HL6+ZWLFirFyZOpXF9esn4ebWECEhd5ljzZp1gq/vc7Rq1a2AM1UHp/BVgJ6uLlZPmYLbPj4iya7O3rqFmV5eapQsf6pXrozDK1fiyeHD6NKiBVo3bIheudL9ZmZlITo+Xk0SahYpaWnoP28eszivr6eH03//jcoVKiik/90nT2LTkSNyZz9lz/Bz13yQBisrO1SsyA+vSU7+pVBvHTb16jXHoUPBIgvF587tx6hRzfHmzXOljCkNlFK8eHEf8+YNxLx5A/D7Nz/Lq7a2NqZMWYOtWwNUFlQlCZzCVyEtGzTA0yNHmILpxoaGWD99upqlKphGtWsjYNs2XNyyJY9ZwvfyZVTv3RvjV63Cm48f1SSh+klJS0NvDw+R7Kl7lywRyeMjDzweDyv37oXHpk2o0bcvgoKDZe9LQTN8QohImcKgoJMy91UY5uaW2LXrBnr2HMkce/s2BCNGNMW+fWvUUkQnNTUZp07tgbt7E4we3RLXr59gPrOwsMLu3bcwevQCuW6qyoBT+CrGsFQpbJs/H1e2bcPuhQvzFMDIkqOakTIxzpXHJjs7G+sEPvs+p0+j9sCBGDR/vkgkaEngV1ISuk2fLqLs10yZImIOk5cbjx8zQXFmpqZo3bChzH2xi+LIumibQ6dOA5ntwEB/pXqg6esbYNmy/Zg3bxv09fkmsqysTOzYsQiurnWxe/efeP/+ldLGz+H9+1fYsGE6XFwssXr1BISHi2Zz7d59OI4efaaQ6GNlwCl8NdG5RQsM6do1z/Flu3ah2/TpGufJk5tvP3+KePRQSvHvtWtoOmIEnCdPRuD9+xp541I0RqVLi+SYXz15MhaMHl3AGdJz8MIFZtvNxUWuPEg8BZl0AL59ukyZ8gCAuLgoEdu1MiCEwNV1Co4ceYa6dYW1gD99eoPdu5dj0CB7DBnSAD4+qxTmKgrwi5RcvXockyZ1xKBB9vD334rk5F/M5/r6BujVazQOHQrGihUHYWysuIL3ikazM2iVMJ6Fh2OdoDRhXVdX/DV1KiYPGqT0PO2yUKFcOdzdtw//PX2KdQcO4OKdO8xn1x4+xLWHD1GjShWM7tULI3r0kKmUX1FAS0sLe5csgYObGxaPGYP5o0YptP+klBSm2hoAjOjRQ67+slgzfG1t+X7+Ojq66NhxAE6d2g0ACAjwQ8OGreXqUxKsrWth7947OHhwPfbvX4PUVGGMQ0TEC0REvMDOnUtgZ9cQnTu7on37vjA3t0Tp0sYF/pays7MRHf0eb9+GCF7P8fZtSL71e6tVs8WAAX+gZ8+RTOUuTYdo8izM0d6eBh86pG4xVIa3nx9menmJzIxbOThgz+LFqFO9egFnqp/nb95g/cGD8LtyJc+jvaW5OT5fuKCRNy5pyMrKgm9AAIZ07Zpnlh33/btIwXZFcejCBYxYtgwAvzh8qL+/XC6es7y8sNmXn89l5kwvuLt7yCVfcHAQJk3qCAAoV64CLl6MUmlQUWpqMm7fvoDAwGO4c+dCod47hobGMDIqA0NDExgZlYGRURnExHyEsbEpIiJeICUlqcDztbW10bZtHwwc+AeaNu2okd9pR0fymFIqNlCDm+FrENOHDEFTe3uMXbmS8fa48/w5Grq5YdGYMfAcNUrq/OeqwsHODkdWrcKqP/7AxiNHcOjiRSQm8X88w1xc8vwwfiUlwdjQUGP80wvj6oMHmOnlhZfv3uFjTAwWjxsn8rkylD0A7DopXAwd2aOH3NdLkSYdAGjUqC3Kl6+EhISv+P49DvfvB6B1a/meQqShVClDdO7sis6dXZGSkoRbt87h6tVjuHv3EjIy0vO0T07+jeTk31KNoa2tDWvrOujYsT/69ZuAChWkKzqjSXAzfA0kPSMDq/ftw9r9+0Uewe1tbLBr4UK5Fu1URWpaGk7fuIG9Z89i69y5eZ5Quk+fjjefPqGbkxO6tmyJDo6OMCwlX151RZOZlYVbT57A288PZ2/dYo7r6ujg2dGjsGelo1AGt589QxvBjUVXRwcfzp2Dpbm5XH1OWbcOO44fBwDMm7cNrq5T5JZz8+Y5OHyY717crJkzduxQbOStLCQl/cKtW2cRGHgM4eFPkJSUmO/s3dLSGjExH0EphampGezsHGBr6wBb2waoWbMBbGzsmQCwokBBM3xO4WswIW/fYvyqVUyN1xzG9O4N77lzNU5BSkpUXByq9ewpYvrR09VFm0aN0LVFC7g4OaFejRpqmf2npqUh8MEDnAwKwrn//mOCqHIwKl0ai8aMwcyhQ2Ggr1wl0GvWLJz/7z8A/P/53qVL5e5z4urV2H3qFADA0/MfDBw4Se4+o6M/oG/fGsz/08/vBWrWVIxLqiLh8XhISfmNpKREJCf/QlJSIpKSEqGjowcdHV1YW9dC+fKVisxTZ35wJp0iSgNbW9zdtw/bjx/Hwu3bkZyaCgAIiYiAgZ56Q7Tl4Wl4OErp6zN/DwBkZGYyi73zvL1haW6Ozs2bo3m9evhj4MACelMMlFK4L1mCMzdvisjFZlSvXlgzZYpKCsGHRkQwyp4QgrkjRhRyhmSwnxgVZWu3tLRG+/b9GF90P78tWLx4j0L6ViTa2towNjbVaC8aZaN5Kw4cImhra2P6kCF4dfw4+nfoAC0tLexcoHkBHdLQs00bfLt6FYHbt2PO8OGoV6NGnjbR8fE4cP48Y35g8yw8HFt8fXH1wQOEf/iAL7Gx+J6YiLT0dLGuoJRS/Pj1CyFv3+L8f/9hx/HjOHPjhkgbQgjif/zIo+yrVKyIaYMH48nhw9i/bJlKlD0AfP/1izGD9W3fHrWtrRXSr6gNX3HzPTe3mcz2xYuH8OMHF4GtiXAz/CJC1UqVcGLDBryMjETdXAqSx+Nhi68vJvTvr7CsjMrGQF8fzs2bw7l5c2yYMQNfYmNx5f59BNy/j8AHD/DjF9/Pua4YO/mF27ex+J9/xParpaWF0gYGzOtjTAxKGxjkUeS92rRBn/btRY7179ABgQ8ewK5aNfTv2BH9O3SAo729Wh7x2zZujFB/f5y7dQvWlpYK6zdLgYFXbBwcWsHe3hFhYcHIyEjHv//+g/Hj5TdBcSgWTuEXMXIre4DvyTF782ZsPHoUmzw8MLBTpyJnh6xSsSLG9OmDMX36gMfj4eHLl7gXEiKSeyiHsAKKyWRnZyMpJQVJKSnMMXEmms9iyjkO7tIFbRs3Rp3q1TXi+mlpaeW5KckLT8QPX3EKnxACN7dZWLx4GADgyJGNcHWdWmT800sKnMIv4iT8/IlFO3YA4C+Gunp6om3jxtjs4SFTCTxNQFtbGy0bNEDLBg3Eft69VSsY6Okh7P17xP/4gdT0dKSkpSElLS1PdSlrS0t8iI6GYalSqFqxIqpVqoRqlSqhjhgTSVkTkzx1iYsbojZ8xbr4OjsPwu7df+LTpzdISkrE//73F2bMKFrlCIsyoaEPcfjw3wW24RR+EadcmTLYOncuZm/ejLjv3wEAt548QZPhwzG2Tx+s+uMPVCxfXs1SKpZh3brlm6smKyuLUf4paWnQ0tKCiaEhypqYaMSsvSAyMjPhf+UK3FxclLZGoyyTDsC/gUyevBqenvzkgP7+3hg8eCoqVcr7lMahWG7fvgBPT1ekpaUU2E7eIuaDCCEvCSHZhJB8S/AQQj4QQl4QQp4RQmRP9ceRB0II3Lt3R/iJE5gxdCh0BIqCUgqf06dh278/1h84gPSMDDVLqhp0dHRgYmSESmZmsKlSBdaWlihXpozGK3uAH2k9YtkyNBs5EvfYxbsViCJTK4ijU6cBsLdvCgDIyEjH8uWjRMxIHIrn9GkfzJ7dp1BlD8jvpRMKoD+AW4U1BNCBUtowP/9QDvkwNTbG5tmz8cLfH91btWKO/05OxvytW2E/aBDO3ZLk38ShDqLj4/HnHr4r45PXr3H/xQuljMNTssInhGD27M1MZHVwcBAOHdpQyFkcspCdnY1du5Zh1arxzP/V0tK6wHPkUviU0leU0nB5+uBQLLWtrXFhyxZc8vYWceV7FxWFRyUsdXFRITs7G1PWrWMWmu1tbDB18GCljJWlpEVbNg4OThg9Wlhk/Z9/FuPevQCljFVS+fkzAR4evbFnzwrmWO3ajbFv370Cz1OVHz4FcIUQ8pgQMqGghoSQCYSQYEJIcPyPHyoSr/jh4uSEED8/bJkzB2VNTGBhZoZ5Cgre4VAsnlu34jQrLmDr3LlypUAuCGWbdHIYP34p6tdvAYD/VOHpOUgjKlQVB0JDH8DdvTFu3xamzW7Zsit27boBM7OCs9IWqvAJIVcJIaFiXn2kkLEVpbQxgG4AphBC2ubXkFK6m1LqSCl1NC9bVoohOHKjq6OD6UOG4O3Jkzi5YUMeH/2XkZEYvmQJIj5/VpOEHFt8fbGBlT5kqqsrOjZtqrTxlBV4lRsdHV2sX3+CVQbxN2bO7IG4uCiljVncoZTC13cLxo1rg69fPzHHhw+fg02bzsHQ0LjQPgpV+JRSZ0ppPTGvM1IIGi14jwNwCkCzgs/gUCTlTU3Ron79PMeX7NyJw5cuofbAgZiwejU+f/2qBulKLv5XrmDWxo3Mfu+2bbHJQ750xYXBLgeo7Ghtc3NLbNlyAYaGfFfXuLgozJzZU+pslRxATMxHTJ/eDV5eM5GVxXc9NjY2hZfXGcyYsUFiF1ulm3QIIYaEEOOcbQBdwF/s5VAjbz5+xKmgIAD8R+49p07Btn9/zPLyYtw7OZRHUHAwRixbxqSCcGrQAL5r1ig9l7yqTDo51KxZH+vW/cuM9ebNMyxYMFgtdWiLItnZ2fD33wZX17oi6yD29k1x5MhTtGvXW6r+5HXL7EcI+QKgJYALhJAAwXFLQshFQbOKAG4TQp4DeAjgAqX0sjzjcsiPnZUVbvv4oF3jxsyx9IwMbPb1RfXevTHTywufuBm/Unj76RP6zp7NBInVtrbGuU2bUNrAQOljq8qkw6ZFi85YsGAns3/37iWsXTtJqTVwiwOvXj3GuHGtsWHDNKaqFyEEw4Z5wMfnv0I9csQhr5fOKUppFUqpPqW0IqW0q+B4NKW0u2D7HaXUQfCqSyldLc+YHIqjVcOGCNq1C1e2bUOzunWZ4ylpadji64saffpg1PLlCHv3To1SFj+qW1oydnpLc3Nc3roV5cqUUcnYygy8Koi+fcdizJhFzP6ZM3uxZs1ETumL4fv3OKxaNR4jRjRFSIjQ68bGxh779t3FrFleMufn57JllnAIIejcogXu/+9/OOPlBQc7O+azLB4PB86fx9wtW9QoYfFDR0cHvqtXY2CnTrjk7Q0rCwuVja2sXDqS8McfK9Gjh9BT7PRpHyxYMBg/fyaoVA5NJSsrE0eObEL//nY4fdqHMffp6Ohi3LglOHz4CeP5JCucwucAwFf8vdu1w9MjR3DJ21vE1OOp4MLcHPxsocfXrUMDW1uVjqsOk04OhBAsXboPPXuOZI5du/YvXF3tce3aCZXKokmkp6fh1Kk9cHWti02bPJCUJCy607p1D/j7h2LSpBUKqbrF5dLhEIEQAhcnJ7g4OeFeSAjO3rqFNo0aibT5lZSETpMnY2iXLhjeowc499n8+fn7Nzy3boWJoSHWz5ihbnHUZtLJQVtbG0uX7kOpUoY4fpyf9O/79zjMnz8QnToNxLx521C+fEWVy6UOfv36gRMndsLPbwsSEkSzt1arZofZszejVSvxOaNkhVP4HPmSX8ZK/8BABIeFITgsDJ7btqFv+/YY17cvnJs1y1OsvKRCKcWxwEDM8PJCbALfZFG+TBnMV/PTkjpNOjloaWlh/vztcHLqhjVrJiI+PhoAf7YfHHwds2dvQbduw4pE/iNZ+Pr1E3x9t+DUqd156uwaGZXBuHFLMHjwNOjqKr6qHafwOaTm6GWhk1VmVhaOX72K41evwtrSEmN798bo3r1RuUIFNUqoXp6Fh2OGlxduPXkicvzeixfIyspSuutlQbBNOuqY4bNp06Ynjh17ic2b5+DMmb0AgMTE71i6dDjOnNmLuXO3amRtXFmglOL587vw89uCoKCTeRLKVahQGW5uHujXb7xEAVSywhUx55CapJQUHAsMhM+ZM2KzOmppaaF7q1ZwdXZGr7ZtYWqsvC+wJhGbkIDF//yDvWfOiJRatDAzw9a5c9G/Y0e1z1qr9uiBL4LiL+fPf9SY1MX37wdi1apxIhGk2traGDRoCgYNmgIrK7sCztZMKKV4+fIRrl8/gevXT+DLl8g8bWxs6mLEiLno2nWowmb0BRUx5xQ+h1yEvXsHn9OnceDCBXxPTMzz+SVvb7g4OalBMtWRnpEBbz8/rNy7F7+Tk5njOtramOLqij8nTkQZIyM1SijE0sUFMd++AQAuXvyCChUqq1kiIcnJv7Fz51IcO7Y1zwy4Tp0m6NrVDV27DoG5ueJKPioaHo+H58/v4Pr1EwgKOonY2C9i2zVt2hFubrPQqlV3hZtBOYXPoXTS0tNxKigIe06fRlAwv+SBiaEh4q9ehZ6uMOw7OTUVW/39FVqYW920GDUKD0JFg8e7OTlho4eHxv2NFbt0YSKpL1+OKTTZljqIiAjF339PR3BwUJ7PCCFwdOyArl3d0LFjf5iYqN9h4Pv3OISFPcKtW+dw8+bpPAuwORgaGsPZ2RVDh85AzZp5U50oCk7hc6iUiM+f8e+1a0jPyMCyCaLJUU9ev44B8+YBAKwsLNDKwYF51atRQ20LiZJAKcXP37/zlEGc7+2N9QcPAgBqWVlhk4cHurFqEmgSZp06IUHwJBYYGIeyZc3VLJF4KKX477/zOHt2H+7cuYjMzLwFfHR19dCqVXe0aNEFNjZ1YWNTF6amyq3u9vNnAl6/foywsGC8evUYr14Fi5ihcmNiUhbt2vVBx44D0KyZM/T1lR9NzSl8Do1h+JIlOHzpktjPjA0N4dSgAdo2aoS2jRujqb099PUU76kgDTweDyFv3+Lfa9fgHxgIa0tLXBXUEM7hyevX6D5jBjxHjsQUV1elpTZWBGU7dMDP3/zkZdeuJRSJIuO/fv3A9esnERBwFMHBQShIZ5UvXwk1atRDjRr1ULNmfdSoUQ9WVnYoVcoQ2to6Ba6hUEqRnPwL3759RULCV3z7FsO8R0e/x6tXwYiKel+ovOXLV0T79v3QseMANGnSTuG1gwuDU/gcGsPJ69dx9PJlXL53D8mpqYW2r1ejBmpZWaG2tTVqWVnxX9bWSrGJ//j1CyFv3/JfERF4/uYNQiMjkZqezrTR0tJC9KVLInWCKaXIzs7W6KeTHMq0a4dfgnWGoKAfMDY2VbNE0hEfH40rV/wREHAUYWHSVUvV0tKCnp4B9PT0oadnAH19A+jq6kNPT59R9OnphX8nc6Ovb4BatRqhfv2WaN++Lxo0cFLrd4FT+BwaR2ZWFp6Fh+PO8+fMK2cxsTC6OTnhore3yLEzN27g9YcPKGNkBBMjI/67oSGMSpVCano6fqek4HdyMpJSU9G6YUPUrFpVRBbbfv3wMSam0LFNDA1xYv16ODdvLt0frCEYtWnD3Ghv3vylVBdAZfPhQzhu3TqLiIgXiIwMxYcPr5CenqbUMXV19WBr6wB7e0fUqeMIe3tHVK9ur1ZX29wUpPA1R0qOEoWujg6a1q2LpnXrYqabGyileB8Vhf+ePcOtJ09w88kT/EpOhriqZ+IWQv0DA+EbIFkZvb1LlogofF0dHZGF5dxYmJmhfZMmGNy5M7q2bAkDfflD3NWFaGoFzX8iKQhr61qwtp7L7PN4PERFvUNkZCgiI0OZG0FMzEdkZKRJlKhNX78UzMwsUL58JZF3MzML2No2gK1tA6UERKkKTuFzaASEENhUqQKbKlUwsmdPAPy0BG8+fsTrDx8QznqvX7NmnvMTk5LyHMuP34LasWwcbG3xMSYGdW1s4GBnhwY1a8LBzg71a9YsVqkjeGpOraBMtLW1Ua2aLapVs0WHDv3yfJ6VlYXMzHSkp6chIyMNGRnpgvc0GBgYwszMAoaGxmqPlVAmnMLn0FhMjY3RrF49NKtXeLSla+fOsLexQWJSEn4lJSExORm/kpKQlJqK0gYGMCpVCsaGhjAuXRq1rKzynL9zwQIcXb1aoxdcFUE2y4Rb0tJg6OjoQEdHB6VKGapbFLVRvL/dHCWGnKcCWSlvWrQWL2WlOM/wOQqnZN3iOThKMLkdNEraDJ+DU/gcHCUG0dk999MviXD/dQ6OEoKo/Z4z55RE5C1ivoEQ8poQEkIIOUUIEWsIJYS4EELCCSERhBBPecbk4OCQjWyR1MjcXK8kIu9/PRBAPUppAwBvACzI3YAQog1gO4BuAOwBDCWE2Ms5LgcHh5SwffAJ4RR+SUQuLx1K6RXW7n0AA8U0awYgglL6DgAIIX4A+gAIk2dsDg4O6SCEoKm9PZJSCXSNy6hbHA41oEi3zDEA/MUcrwzgM2v/C4B849IJIRMA5CARfVQAAAUQSURBVKRYTCeOjqH5tdUgzABIlhdA/XCyKociJ6ujY5EIMCpy11XdQgDIG2gioFCFTwi5CkBc0uxFlNIzgjaLAGQBOCKuCzHH8k3gQyndDWC3oN/g/HJCaBJFRU6Ak1VZcLIqB05WxVKowqeUOhf0OSFkJICeADpR8ZnYvgCoytqvAiBaGiE5ODg4OORHXi8dFwDzAfSmlOZNUMLnEQBbQkh1QogegCEAzsozLgcHBweH9Mi7VL8NgDGAQELIM0LITgAghFgSQi4CAKU0C8BUAAEAXgE4Ril9KWH/u+WUT1UUFTkBTlZlwcmqHDhZFYhG58Pn4ODg4FAcnDMuBwcHRwmBU/gcHBwcJQSNUvhFJVUDIWQQIeQlISSbEJKvGxYh5AMh5IVgfUO6ApwKQgpZ1Z7+ghBSjhASSAh5K3gXW3mEEMITXNNnhBCVOgAUdp0IIfqEEH/B5w8IIdaqlC+XLIXJOooQEs+6luPUJOc+QkgcIURszA3h4y34O0IIIY1VLSNLlsJkbU8ISWRd06WqlrFAKKUa8wLQBYCOYHsdgHVi2mgDiARgA0APwHMA9iqWsw6AWgBuAHAsoN0HAGZqvqaFyqoJ11Qgx3oAnoJtT3H/f8FnSWq6loVeJwCTAewUbA8B4K/Bso4CsE0d8uWSoy2AxgBC8/m8O4BL4Mf0tADwQINlbQ/gvLqvaX4vjZrhU0qvUL5XD8BP1VBFTDMmVQOlNANATqoGlUEpfUUpDVflmLIioaxqv6YC+gA4INg+AKCvGmQoCEmuE/tv+BdAJ6Kemnma8j8tFErpLQDfC2jSB8BByuc+AFNCiIVqpBNFAlk1Go1S+LkYA/5dPTfiUjVUVolE0kMBXCGEPBakjNBUNOWaVqSUxgCA4L1CPu0MCCHBhJD7hBBV3hQkuU5MG8HkJRFAeZVIl48cAvL7nw4QmEn+JYRUFfO5JqAp309JaUkIeU4IuUQIqatuYdiovMShqlM1yIokckpAK0ppNCGkAvixCq8FMwSFogBZVXJNgYJllaKbaoLragPgOiHkBaU0UjESFogk10ll17IQJJHjHABfSmk6IWQS+E8mHZUumfRoyjWVhCcArCilSYSQ7gBOA7BVs0wMKlf4tIikaihMTgn7iBa8xxFCToH/mK1wha8AWVWW/qIgWQkhsYQQC0ppjOCRPS6fPnKu6ztCyA0AjcC3VysbSa5TTpsvhBAdAGWgHhNAobJSShNYu3vAXzfTRIpMehZK6S/W9kVCyA5CiBmlVBOSqmmWSac4pWoghBgSQoxztsFfkNbUzJ+ack3PAhgp2B4JIM/TCSGkLCFEX7BtBqAVVJdqW5LrxP4bBgK4ns/ERdkUKmsuO3hv8CPhNZGzAEYIvHVaAEjMMf1pGoSQSjlrNoSQZuDr2ISCz1Ih6l41zrXCHQG+re6Z4JXj7WAJ4CKrXXfwC65Egm+2ULWc/cCfdaQDiAUQkFtO8L0jngteL9Uhp6SyasI1FchQHsA1AG8F7+UExx0B+Ai2nQC8EFzXFwDGqljGPNcJwArwJykAYADguOC7/BCAjTqupYSyrhV8N58DCAJQW01y+gKIAZAp+K6OBTAJwCTB5wT8IkqRgv95vp5xGiDrVNY1vQ/ASV2yintxqRU4ODg4SggaZdLh4ODg4FAenMLn4ODgKCFwCp+Dg4OjhMApfA4ODo4SAqfwOTg4OEoInMLn4ODgKCFwCp+Dg4OjhPB/VRXSRwDtnQYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(features, labels, svm_rbf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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